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                 "debugpy": "{\"ncore_pip_deps_311_debugpy_cp311_cp311_macosx_14_0_universal2_36f48298\":[{\"filename\":\"debugpy-1.8.12-cp311-cp311-macosx_14_0_universal2.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_debugpy_cp311_cp311_manylinux_2_5_x86_64_a28ed481\":[{\"filename\":\"debugpy-1.8.12-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_debugpy_cp311_cp311_win_amd64_4703575b\":[{\"filename\":\"debugpy-1.8.12-cp311-cp311-win_amd64.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_debugpy_py2_none_any_274b6a20\":[{\"filename\":\"debugpy-1.8.12-py2.py3-none-any.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_debugpy_sdist_646530b0\":[{\"filename\":\"debugpy-1.8.12.tar.gz\",\"version\":\"3.11\"}]}",
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                 "dracopy": "{\"ncore_pip_deps_311_dracopy_cp311_cp311_macosx_10_9_x86_64_c4131151\":[{\"filename\":\"dracopy-2.0.0-cp311-cp311-macosx_10_9_x86_64.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_dracopy_cp311_cp311_macosx_11_0_arm64_e16575ab\":[{\"filename\":\"dracopy-2.0.0-cp311-cp311-macosx_11_0_arm64.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_dracopy_cp311_cp311_manylinux_2_24_aarch64_418964da\":[{\"filename\":\"dracopy-2.0.0-cp311-cp311-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_dracopy_cp311_cp311_manylinux_2_24_x86_64_fa9a3215\":[{\"filename\":\"dracopy-2.0.0-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_dracopy_cp311_cp311_win_amd64_106c968f\":[{\"filename\":\"dracopy-2.0.0-cp311-cp311-win_amd64.whl\",\"version\":\"3.11\"}]}",
                 "embreex": "{\"ncore_pip_deps_311_embreex_cp311_cp311_macosx_10_9_x86_64_8154ab34\":[{\"filename\":\"embreex-2.17.7.post7-cp311-cp311-macosx_10_9_x86_64.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_embreex_cp311_cp311_manylinux_2_12_x86_64_c4775a4d\":[{\"filename\":\"embreex-2.17.7.post7-cp311-cp311-manylinux2010_x86_64.manylinux_2_12_x86_64.manylinux_2_28_x86_64.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_embreex_cp311_cp311_win_amd64_a1157976\":[{\"filename\":\"embreex-2.17.7.post7-cp311-cp311-win_amd64.whl\",\"version\":\"3.11\"}]}",
@@ -20059,7 +21755,9 @@
                 "fasteners": "{\"ncore_pip_deps_311_fasteners_py3_none_any_758819cb\":[{\"filename\":\"fasteners-0.19-py3-none-any.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_fasteners_sdist_b4f37c3a\":[{\"filename\":\"fasteners-0.19.tar.gz\",\"version\":\"3.11\"}],\"ncore_pip_deps_38_fasteners_py3_none_any_1d4caf5f\":[{\"filename\":\"fasteners-0.18-py3-none-any.whl\",\"version\":\"3.8\"}],\"ncore_pip_deps_38_fasteners_sdist_cb7c13ef\":[{\"filename\":\"fasteners-0.18.tar.gz\",\"version\":\"3.8\"}]}",
                 "fastjsonschema": "{\"ncore_pip_deps_311_fastjsonschema_py3_none_any_c9e5b7e9\":[{\"filename\":\"fastjsonschema-2.21.1-py3-none-any.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_fastjsonschema_sdist_794d4f0a\":[{\"filename\":\"fastjsonschema-2.21.1.tar.gz\",\"version\":\"3.11\"}]}",
                 "filelock": "{\"ncore_pip_deps_311_filelock_py3_none_any_96f5f634\":[{\"filename\":\"filelock-3.29.0-py3-none-any.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_filelock_sdist_69974355\":[{\"filename\":\"filelock-3.29.0.tar.gz\",\"version\":\"3.11\"}]}",
+                "fire": "{\"ncore_pip_deps_311_fire_py3_none_any_e43fd8a5\":[{\"filename\":\"fire-0.7.1-py3-none-any.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_fire_sdist_3b208f05\":[{\"filename\":\"fire-0.7.1.tar.gz\",\"version\":\"3.11\"}]}",
                 "flatbuffers": "{\"ncore_pip_deps_311_flatbuffers_py2_none_any_7634f50c\":[{\"filename\":\"flatbuffers-25.12.19-py2.py3-none-any.whl\",\"version\":\"3.11\"}]}",
+                "fonttools": "{\"ncore_pip_deps_311_fonttools_cp311_cp311_macosx_10_9_universal2_7ef04bc7\":[{\"filename\":\"fonttools-4.56.0-cp311-cp311-macosx_10_9_universal2.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_fonttools_cp311_cp311_macosx_10_9_x86_64_ffda9b8c\":[{\"filename\":\"fonttools-4.56.0-cp311-cp311-macosx_10_9_x86_64.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_fonttools_cp311_cp311_manylinux_2_17_aarch64_e2e993e8\":[{\"filename\":\"fonttools-4.56.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_fonttools_cp311_cp311_manylinux_2_17_x86_64_003548ea\":[{\"filename\":\"fonttools-4.56.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_fonttools_cp311_cp311_musllinux_1_2_aarch64_bd982582\":[{\"filename\":\"fonttools-4.56.0-cp311-cp311-musllinux_1_2_aarch64.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_fonttools_cp311_cp311_musllinux_1_2_x86_64_b23d30a2\":[{\"filename\":\"fonttools-4.56.0-cp311-cp311-musllinux_1_2_x86_64.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_fonttools_cp311_cp311_win_amd64_14a3e3e6\":[{\"filename\":\"fonttools-4.56.0-cp311-cp311-win_amd64.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_fonttools_py3_none_any_1088182f\":[{\"filename\":\"fonttools-4.56.0-py3-none-any.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_fonttools_sdist_a114d156\":[{\"filename\":\"fonttools-4.56.0.tar.gz\",\"version\":\"3.11\"}]}",
                 "fsspec": "{\"ncore_pip_deps_311_fsspec_py3_none_any_7c771235\":[{\"filename\":\"fsspec-2025.10.0-py3-none-any.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_fsspec_sdist_b6789427\":[{\"filename\":\"fsspec-2025.10.0.tar.gz\",\"version\":\"3.11\"}],\"ncore_pip_deps_38_fsspec_py3_none_any_efb87af3\":[{\"filename\":\"fsspec-2025.3.0-py3-none-any.whl\",\"version\":\"3.8\"}],\"ncore_pip_deps_38_fsspec_sdist_a935fd1e\":[{\"filename\":\"fsspec-2025.3.0.tar.gz\",\"version\":\"3.8\"}]}",
                 "gast": "{\"ncore_pip_deps_311_gast_py3_none_any_52b18231\":[{\"filename\":\"gast-0.6.0-py3-none-any.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_gast_sdist_88fc5300\":[{\"filename\":\"gast-0.6.0.tar.gz\",\"version\":\"3.11\"}]}",
                 "google_pasta": "{\"ncore_pip_deps_311_google_pasta_py3_none_any_b3248279\":[{\"filename\":\"google_pasta-0.2.0-py3-none-any.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_google_pasta_sdist_c9f2c8df\":[{\"filename\":\"google-pasta-0.2.0.tar.gz\",\"version\":\"3.11\"}]}",
@@ -20076,12 +21774,14 @@
                 "ipython_pygments_lexers": "{\"ncore_pip_deps_311_ipython_pygments_lexers_py3_none_any_a9462224\":[{\"filename\":\"ipython_pygments_lexers-1.1.1-py3-none-any.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_ipython_pygments_lexers_sdist_09c01380\":[{\"filename\":\"ipython_pygments_lexers-1.1.1.tar.gz\",\"version\":\"3.11\"}]}",
                 "jedi": "{\"ncore_pip_deps_311_jedi_py2_none_any_a8ef22bd\":[{\"filename\":\"jedi-0.19.2-py2.py3-none-any.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_jedi_sdist_4770dc3d\":[{\"filename\":\"jedi-0.19.2.tar.gz\",\"version\":\"3.11\"}]}",
                 "jinja2": "{\"ncore_pip_deps_311_jinja2_py3_none_any_aba0f4dc\":[{\"filename\":\"jinja2-3.1.5-py3-none-any.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_jinja2_sdist_8fefff8d\":[{\"filename\":\"jinja2-3.1.5.tar.gz\",\"version\":\"3.11\"}]}",
+                "joblib": "{\"ncore_pip_deps_311_joblib_py3_none_any_5fc3c503\":[{\"filename\":\"joblib-1.5.3-py3-none-any.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_joblib_sdist_8561a326\":[{\"filename\":\"joblib-1.5.3.tar.gz\",\"version\":\"3.11\"}]}",
                 "jsonschema": "{\"ncore_pip_deps_311_jsonschema_py3_none_any_fbadb6f8\":[{\"filename\":\"jsonschema-4.23.0-py3-none-any.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_jsonschema_sdist_d71497fe\":[{\"filename\":\"jsonschema-4.23.0.tar.gz\",\"version\":\"3.11\"}]}",
                 "jsonschema_specifications": "{\"ncore_pip_deps_311_jsonschema_specifications_py3_none_any_a09a0680\":[{\"filename\":\"jsonschema_specifications-2024.10.1-py3-none-any.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_jsonschema_specifications_sdist_0f38b836\":[{\"filename\":\"jsonschema_specifications-2024.10.1.tar.gz\",\"version\":\"3.11\"}]}",
                 "jupyter_client": "{\"ncore_pip_deps_311_jupyter_client_py3_none_any_e8a19cc9\":[{\"filename\":\"jupyter_client-8.6.3-py3-none-any.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_jupyter_client_sdist_35b3a094\":[{\"filename\":\"jupyter_client-8.6.3.tar.gz\",\"version\":\"3.11\"}]}",
                 "jupyter_core": "{\"ncore_pip_deps_311_jupyter_core_py3_none_any_4f7315d2\":[{\"filename\":\"jupyter_core-5.7.2-py3-none-any.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_jupyter_core_sdist_aa5f8d32\":[{\"filename\":\"jupyter_core-5.7.2.tar.gz\",\"version\":\"3.11\"}]}",
                 "jupyterlab_pygments": "{\"ncore_pip_deps_311_jupyterlab_pygments_py3_none_any_841a8902\":[{\"filename\":\"jupyterlab_pygments-0.3.0-py3-none-any.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_jupyterlab_pygments_sdist_721aca4d\":[{\"filename\":\"jupyterlab_pygments-0.3.0.tar.gz\",\"version\":\"3.11\"}]}",
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@@ -20090,6 +21790,7 @@
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                 "markupsafe": "{\"ncore_pip_deps_311_markupsafe_cp311_cp311_macosx_10_9_universal2_9025b401\":[{\"filename\":\"MarkupSafe-3.0.2-cp311-cp311-macosx_10_9_universal2.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_markupsafe_cp311_cp311_macosx_11_0_arm64_93335ca3\":[{\"filename\":\"MarkupSafe-3.0.2-cp311-cp311-macosx_11_0_arm64.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_markupsafe_cp311_cp311_manylinux_2_17_aarch64_2cb8438c\":[{\"filename\":\"MarkupSafe-3.0.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_markupsafe_cp311_cp311_manylinux_2_17_x86_64_a123e330\":[{\"filename\":\"MarkupSafe-3.0.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_markupsafe_cp311_cp311_musllinux_1_2_aarch64_d8213e09\":[{\"filename\":\"MarkupSafe-3.0.2-cp311-cp311-musllinux_1_2_aarch64.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_markupsafe_cp311_cp311_musllinux_1_2_x86_64_0bff5e0a\":[{\"filename\":\"MarkupSafe-3.0.2-cp311-cp311-musllinux_1_2_x86_64.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_markupsafe_cp311_cp311_win_amd64_70a87b41\":[{\"filename\":\"MarkupSafe-3.0.2-cp311-cp311-win_amd64.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_markupsafe_sdist_ee55d3ed\":[{\"filename\":\"markupsafe-3.0.2.tar.gz\",\"version\":\"3.11\"}]}",
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                 "matplotlib_inline": "{\"ncore_pip_deps_311_matplotlib_inline_py3_none_any_d56ce515\":[{\"filename\":\"matplotlib_inline-0.2.1-py3-none-any.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_matplotlib_inline_sdist_e1ee949c\":[{\"filename\":\"matplotlib_inline-0.2.1.tar.gz\",\"version\":\"3.11\"}]}",
                 "mdurl": "{\"ncore_pip_deps_311_mdurl_py3_none_any_84008a41\":[{\"filename\":\"mdurl-0.1.2-py3-none-any.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_mdurl_sdist_bb413d29\":[{\"filename\":\"mdurl-0.1.2.tar.gz\",\"version\":\"3.11\"}]}",
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@@ -20105,6 +21806,7 @@
                 "networkx": "{\"ncore_pip_deps_311_networkx_py3_none_any_d47fbf30\":[{\"filename\":\"networkx-3.6.1-py3-none-any.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_networkx_sdist_26b7c357\":[{\"filename\":\"networkx-3.6.1.tar.gz\",\"version\":\"3.11\"}]}",
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                 "nvidia_cuda_nvrtc_cu12": "{\"ncore_pip_deps_311_nvidia_cuda_nvrtc_cu12_py3_none_manylinux_2_12_x86_64_a0fa9c2a\":[{\"filename\":\"nvidia_cuda_nvrtc_cu12-12.8.61-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_nvidia_cuda_nvrtc_cu12_py3_none_manylinux_2_17_aarch64_b1f376bf\":[{\"filename\":\"nvidia_cuda_nvrtc_cu12-12.8.61-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_nvidia_cuda_nvrtc_cu12_py3_none_win_amd64_9c8887bf\":[{\"filename\":\"nvidia_cuda_nvrtc_cu12-12.8.61-py3-none-win_amd64.whl\",\"version\":\"3.11\"}]}",
@@ -20139,10 +21841,13 @@
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                 "python_dateutil": "{\"ncore_pip_deps_311_python_dateutil_py2_none_any_a8b2bc7b\":[{\"filename\":\"python_dateutil-2.9.0.post0-py2.py3-none-any.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_python_dateutil_sdist_37dd5420\":[{\"filename\":\"python-dateutil-2.9.0.post0.tar.gz\",\"version\":\"3.11\"}]}",
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@@ -20154,9 +21859,10 @@
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                 "scipy": "{\"ncore_pip_deps_311_scipy_cp311_cp311_macosx_10_13_x86_64_92233b2d\":[{\"filename\":\"scipy-1.15.2-cp311-cp311-macosx_10_13_x86_64.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_scipy_cp311_cp311_macosx_12_0_arm64_62ca1ff3\":[{\"filename\":\"scipy-1.15.2-cp311-cp311-macosx_12_0_arm64.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_scipy_cp311_cp311_macosx_14_0_arm64_4c667649\":[{\"filename\":\"scipy-1.15.2-cp311-cp311-macosx_14_0_arm64.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_scipy_cp311_cp311_macosx_14_0_x86_64_a8bf5cb4\":[{\"filename\":\"scipy-1.15.2-cp311-cp311-macosx_14_0_x86_64.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_scipy_cp311_cp311_manylinux_2_17_aarch64_6a8e34cf\":[{\"filename\":\"scipy-1.15.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_scipy_cp311_cp311_manylinux_2_17_x86_64_28a0d2c2\":[{\"filename\":\"scipy-1.15.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_scipy_cp311_cp311_musllinux_1_2_aarch64_42dabaaa\":[{\"filename\":\"scipy-1.15.2-cp311-cp311-musllinux_1_2_aarch64.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_scipy_cp311_cp311_musllinux_1_2_x86_64_6f5e296e\":[{\"filename\":\"scipy-1.15.2-cp311-cp311-musllinux_1_2_x86_64.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_scipy_cp311_cp311_win_amd64_597a0c70\":[{\"filename\":\"scipy-1.15.2-cp311-cp311-win_amd64.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_scipy_sdist_cd58a314\":[{\"filename\":\"scipy-1.15.2.tar.gz\",\"version\":\"3.11\"}],\"ncore_pip_deps_38_scipy_cp38_cp38_macosx_10_9_x86_64_5678f88c\":[{\"filename\":\"scipy-1.10.1-cp38-cp38-macosx_10_9_x86_64.whl\",\"version\":\"3.8\"}],\"ncore_pip_deps_38_scipy_cp38_cp38_macosx_12_0_arm64_39becb03\":[{\"filename\":\"scipy-1.10.1-cp38-cp38-macosx_12_0_arm64.whl\",\"version\":\"3.8\"}],\"ncore_pip_deps_38_scipy_cp38_cp38_manylinux_2_17_aarch64_bce5869c\":[{\"filename\":\"scipy-1.10.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl\",\"version\":\"3.8\"}],\"ncore_pip_deps_38_scipy_cp38_cp38_manylinux_2_17_x86_64_07c3457c\":[{\"filename\":\"scipy-1.10.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\",\"version\":\"3.8\"}],\"ncore_pip_deps_38_scipy_cp38_cp38_win_amd64_049a8bbf\":[{\"filename\":\"scipy-1.10.1-cp38-cp38-win_amd64.whl\",\"version\":\"3.8\"}],\"ncore_pip_deps_38_scipy_sdist_2cf9dfb8\":[{\"filename\":\"scipy-1.10.1.tar.gz\",\"version\":\"3.8\"}]}",
                 "setuptools": "{\"ncore_pip_deps_311_setuptools_py3_none_any_e3982f44\":[{\"filename\":\"setuptools-75.8.0-py3-none-any.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_setuptools_sdist_c5afc8f4\":[{\"filename\":\"setuptools-75.8.0.tar.gz\",\"version\":\"3.11\"}]}",
-                "shapely": "{\"ncore_pip_deps_311_shapely_cp311_cp311_macosx_10_9_x86_64_91121757\":[{\"filename\":\"shapely-2.1.2-cp311-cp311-macosx_10_9_x86_64.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_shapely_cp311_cp311_macosx_11_0_arm64_16a9c722\":[{\"filename\":\"shapely-2.1.2-cp311-cp311-macosx_11_0_arm64.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_shapely_cp311_cp311_manylinux_2_17_aarch64_cc4f7397\":[{\"filename\":\"shapely-2.1.2-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_shapely_cp311_cp311_manylinux_2_17_x86_64_136ab87b\":[{\"filename\":\"shapely-2.1.2-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_shapely_cp311_cp311_musllinux_1_2_aarch64_16c5d0fc\":[{\"filename\":\"shapely-2.1.2-cp311-cp311-musllinux_1_2_aarch64.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_shapely_cp311_cp311_musllinux_1_2_x86_64_6ddc759f\":[{\"filename\":\"shapely-2.1.2-cp311-cp311-musllinux_1_2_x86_64.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_shapely_cp311_cp311_win_amd64_c64d5c97\":[{\"filename\":\"shapely-2.1.2-cp311-cp311-win_amd64.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_shapely_sdist_2ed4ecb2\":[{\"filename\":\"shapely-2.1.2.tar.gz\",\"version\":\"3.11\"}]}",
+                "shapely": "{\"ncore_pip_deps_311_shapely_cp311_cp311_macosx_10_9_x86_64_5cf23400\":[{\"filename\":\"shapely-2.0.7-cp311-cp311-macosx_10_9_x86_64.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_shapely_cp311_cp311_macosx_11_0_arm64_d8f1da01\":[{\"filename\":\"shapely-2.0.7-cp311-cp311-macosx_11_0_arm64.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_shapely_cp311_cp311_manylinux_2_17_aarch64_8f623b64\":[{\"filename\":\"shapely-2.0.7-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_shapely_cp311_cp311_manylinux_2_17_x86_64_e6d95703\":[{\"filename\":\"shapely-2.0.7-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_shapely_cp311_cp311_win_amd64_b52f3ab8\":[{\"filename\":\"shapely-2.0.7-cp311-cp311-win_amd64.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_shapely_sdist_28fe2997\":[{\"filename\":\"shapely-2.0.7.tar.gz\",\"version\":\"3.11\"}]}",
                 "six": "{\"ncore_pip_deps_311_six_py2_none_any_4721f391\":[{\"filename\":\"six-1.17.0-py2.py3-none-any.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_six_sdist_ff70335d\":[{\"filename\":\"six-1.17.0.tar.gz\",\"version\":\"3.11\"}]}",
                 "snowballstemmer": "{\"ncore_pip_deps_311_snowballstemmer_py2_none_any_c8e1716e\":[{\"filename\":\"snowballstemmer-2.2.0-py2.py3-none-any.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_snowballstemmer_sdist_09b16deb\":[{\"filename\":\"snowballstemmer-2.2.0.tar.gz\",\"version\":\"3.11\"}]}",
                 "soupsieve": "{\"ncore_pip_deps_311_soupsieve_py3_none_any_e72c4ff0\":[{\"filename\":\"soupsieve-2.6-py3-none-any.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_soupsieve_sdist_e2e68417\":[{\"filename\":\"soupsieve-2.6.tar.gz\",\"version\":\"3.11\"}]}",
@@ -20175,6 +21881,7 @@
                 "tensorboard_data_server": "{\"ncore_pip_deps_311_tensorboard_data_server_py3_none_any_7e0610d2\":[{\"filename\":\"tensorboard_data_server-0.7.2-py3-none-any.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_tensorboard_data_server_py3_none_macosx_10_9_x86_64_9fe5d242\":[{\"filename\":\"tensorboard_data_server-0.7.2-py3-none-macosx_10_9_x86_64.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_tensorboard_data_server_py3_none_manylinux_2_31_x86_64_ef687163\":[{\"filename\":\"tensorboard_data_server-0.7.2-py3-none-manylinux_2_31_x86_64.whl\",\"version\":\"3.11\"}]}",
                 "tensorflow": "{\"ncore_pip_deps_311_tensorflow_cp311_cp311_macosx_12_0_arm64_5f964016\":[{\"filename\":\"tensorflow-2.20.0-cp311-cp311-macosx_12_0_arm64.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_tensorflow_cp311_cp311_manylinux_2_17_aarch64_3e9568c8\":[{\"filename\":\"tensorflow-2.20.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_tensorflow_cp311_cp311_manylinux_2_17_x86_64_481499fd\":[{\"filename\":\"tensorflow-2.20.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_tensorflow_cp311_cp311_win_amd64_7551558a\":[{\"filename\":\"tensorflow-2.20.0-cp311-cp311-win_amd64.whl\",\"version\":\"3.11\"}]}",
                 "termcolor": "{\"ncore_pip_deps_311_termcolor_py3_none_any_37b17b5f\":[{\"filename\":\"termcolor-2.5.0-py3-none-any.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_termcolor_sdist_998d8d27\":[{\"filename\":\"termcolor-2.5.0.tar.gz\",\"version\":\"3.11\"}]}",
+                "threadpoolctl": "{\"ncore_pip_deps_311_threadpoolctl_py3_none_any_43a0b8fd\":[{\"filename\":\"threadpoolctl-3.6.0-py3-none-any.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_threadpoolctl_sdist_8ab8b4aa\":[{\"filename\":\"threadpoolctl-3.6.0.tar.gz\",\"version\":\"3.11\"}]}",
                 "tinycss2": "{\"ncore_pip_deps_311_tinycss2_py3_none_any_3a49cf47\":[{\"filename\":\"tinycss2-1.4.0-py3-none-any.whl\",\"version\":\"3.11\"}],\"ncore_pip_deps_311_tinycss2_sdist_10c0972f\":[{\"filename\":\"tinycss2-1.4.0.tar.gz\",\"version\":\"3.11\"}]}",
                 "tomli": "{\"ncore_pip_deps_38_tomli_py3_none_any_939de3e7\":[{\"filename\":\"tomli-2.0.1-py3-none-any.whl\",\"version\":\"3.8\"}],\"ncore_pip_deps_38_tomli_sdist_de526c12\":[{\"filename\":\"tomli-2.0.1.tar.gz\",\"version\":\"3.8\"}]}",
                 "torch": "{\"ncore_pip_deps_311_torch\":[{\"version\":\"3.11\"}],\"ncore_pip_deps_38_torch\":[{\"version\":\"3.8\"}]}",
@@ -20213,16 +21920,20 @@
                 "babel",
                 "beautifulsoup4",
                 "bleach",
+                "cachetools",
                 "cbor2",
                 "certifi",
                 "charset_normalizer",
                 "click",
                 "colorlog",
+                "contourpy",
+                "cycler",
                 "dataclasses_json",
                 "debugpy",
                 "decorator",
                 "defusedxml",
                 "deprecated",
+                "descartes",
                 "docutils",
                 "dracopy",
                 "embreex",
@@ -20232,7 +21943,9 @@
                 "fasteners",
                 "fastjsonschema",
                 "filelock",
+                "fire",
                 "flatbuffers",
+                "fonttools",
                 "fsspec",
                 "gast",
                 "google_pasta",
@@ -20249,12 +21962,14 @@
                 "ipython_pygments_lexers",
                 "jedi",
                 "jinja2",
+                "joblib",
                 "jsonschema",
                 "jsonschema_specifications",
                 "jupyter_client",
                 "jupyter_core",
                 "jupyterlab_pygments",
                 "keras",
+                "kiwisolver",
                 "libclang",
                 "lxml",
                 "manifold3d",
@@ -20263,6 +21978,7 @@
                 "markdown_it_py",
                 "markupsafe",
                 "marshmallow",
+                "matplotlib",
                 "matplotlib_inline",
                 "mdurl",
                 "mistune",
@@ -20278,6 +21994,7 @@
                 "networkx",
                 "numcodecs",
                 "numpy",
+                "nuscenes_devkit",
                 "nvidia_cublas_cu12",
                 "nvidia_cuda_cupti_cu12",
                 "nvidia_cuda_nvrtc_cu12",
@@ -20312,10 +22029,13 @@
                 "ptyprocess",
                 "pure_eval",
                 "pyarrow",
+                "pycocotools",
                 "pycollada",
                 "pydata_sphinx_theme",
                 "pygments",
                 "pynvvideocodec",
+                "pyparsing",
+                "pyquaternion",
                 "pytest",
                 "python_dateutil",
                 "pytz",
@@ -20327,6 +22047,7 @@
                 "roman_numerals",
                 "rpds_py",
                 "rtree",
+                "scikit_learn",
                 "scipy",
                 "setuptools",
                 "shapely",
@@ -20348,6 +22069,7 @@
                 "tensorboard_data_server",
                 "tensorflow",
                 "termcolor",
+                "threadpoolctl",
                 "tinycss2",
                 "tomli",
                 "torch",
diff --git a/deps/pip/BUILD.bazel b/deps/pip/BUILD.bazel
index b031d2fd..1e9c3e3d 100644
--- a/deps/pip/BUILD.bazel
+++ b/deps/pip/BUILD.bazel
@@ -37,6 +37,7 @@ pip_compile(
         ":requirements_colmap.in",
         ":requirements_docs.in",
         ":requirements_ncore.in",
+        ":requirements_nuscenes.in",
         ":requirements_pai.in",
         ":requirements_tests.in",
         ":requirements_tools.in",
diff --git a/deps/pip/requirements_3_11.in b/deps/pip/requirements_3_11.in
index e0c05cc3..e7c68a14 100644
--- a/deps/pip/requirements_3_11.in
+++ b/deps/pip/requirements_3_11.in
@@ -20,6 +20,7 @@
 -r requirements_tools.in
 -r requirements_waymo.in
 -r requirements_colmap.in
+-r requirements_nuscenes.in
 -r requirements_pai.in
 
 # Public API restrictions for 3.11
diff --git a/deps/pip/requirements_3_11.txt b/deps/pip/requirements_3_11.txt
index 548514e2..37fe4126 100644
--- a/deps/pip/requirements_3_11.txt
+++ b/deps/pip/requirements_3_11.txt
@@ -55,6 +55,10 @@ bleach==6.2.0 \
     --hash=sha256:117d9c6097a7c3d22fd578fcd8d35ff1e125df6736f554da4e432fdd63f31e5e \
     --hash=sha256:123e894118b8a599fd80d3ec1a6d4cc7ce4e5882b1317a7e1ba69b56e95f991f
     # via nbconvert
+cachetools==7.1.3 \
+    --hash=sha256:135cfe944bc3c1e805505f65dae0bef375a2f96261171ab66c79ef77d0bda39d \
+    --hash=sha256:9876787e2346e20584d5cca236cb5d49d04e7193de91646f230725b2e1e8b804
+    # via nuscenes-devkit
 cbor2==5.9.0 \
     --hash=sha256:0322296b9d52f55880e300ba8ba09ecf644303b99b51138bbb1c0fb644fa7c3e \
     --hash=sha256:0485d3372fc832c5e16d4eb45fa1a20fc53e806e6c29a1d2b0d3e176cedd52b9 \
@@ -217,6 +221,66 @@ colorlog==6.10.1 \
     --hash=sha256:2d7e8348291948af66122cff006c9f8da6255d224e7cf8e37d8de2df3bad8c9c \
     --hash=sha256:eb4ae5cb65fe7fec7773c2306061a8e63e02efc2c72eba9d27b0fa23c94f1321
     # via trimesh
+contourpy==1.3.1 \
+    --hash=sha256:041b640d4ec01922083645a94bb3b2e777e6b626788f4095cf21abbe266413c1 \
+    --hash=sha256:05e806338bfeaa006acbdeba0ad681a10be63b26e1b17317bfac3c5d98f36cda \
+    --hash=sha256:08d9d449a61cf53033612cb368f3a1b26cd7835d9b8cd326647efe43bca7568d \
+    --hash=sha256:0ffa84be8e0bd33410b17189f7164c3589c229ce5db85798076a3fa136d0e509 \
+    --hash=sha256:113231fe3825ebf6f15eaa8bc1f5b0ddc19d42b733345eae0934cb291beb88b6 \
+    --hash=sha256:14c102b0eab282427b662cb590f2e9340a9d91a1c297f48729431f2dcd16e14f \
+    --hash=sha256:174e758c66bbc1c8576992cec9599ce8b6672b741b5d336b5c74e35ac382b18e \
+    --hash=sha256:19c1555a6801c2f084c7ddc1c6e11f02eb6a6016ca1318dd5452ba3f613a1751 \
+    --hash=sha256:19d40d37c1c3a4961b4619dd9d77b12124a453cc3d02bb31a07d58ef684d3d86 \
+    --hash=sha256:1bf98051f1045b15c87868dbaea84f92408337d4f81d0e449ee41920ea121d3b \
+    --hash=sha256:20914c8c973f41456337652a6eeca26d2148aa96dd7ac323b74516988bea89fc \
+    --hash=sha256:287ccc248c9e0d0566934e7d606201abd74761b5703d804ff3df8935f523d546 \
+    --hash=sha256:2ba94a401342fc0f8b948e57d977557fbf4d515f03c67682dd5c6191cb2d16ec \
+    --hash=sha256:31c1b55c1f34f80557d3830d3dd93ba722ce7e33a0b472cba0ec3b6535684d8f \
+    --hash=sha256:36987a15e8ace5f58d4d5da9dca82d498c2bbb28dff6e5d04fbfcc35a9cb3a82 \
+    --hash=sha256:3a04ecd68acbd77fa2d39723ceca4c3197cb2969633836ced1bea14e219d077c \
+    --hash=sha256:3e8b974d8db2c5610fb4e76307e265de0edb655ae8169e8b21f41807ccbeec4b \
+    --hash=sha256:3ea9924d28fc5586bf0b42d15f590b10c224117e74409dd7a0be3b62b74a501c \
+    --hash=sha256:4318af1c925fb9a4fb190559ef3eec206845f63e80fb603d47f2d6d67683901c \
+    --hash=sha256:44a29502ca9c7b5ba389e620d44f2fbe792b1fb5734e8b931ad307071ec58c53 \
+    --hash=sha256:47734d7073fb4590b4a40122b35917cd77be5722d80683b249dac1de266aac80 \
+    --hash=sha256:4d76d5993a34ef3df5181ba3c92fabb93f1eaa5729504fb03423fcd9f3177242 \
+    --hash=sha256:4dbbc03a40f916a8420e420d63e96a1258d3d1b58cbdfd8d1f07b49fcbd38e85 \
+    --hash=sha256:500360b77259914f7805af7462e41f9cb7ca92ad38e9f94d6c8641b089338124 \
+    --hash=sha256:523a8ee12edfa36f6d2a49407f705a6ef4c5098de4f498619787e272de93f2d5 \
+    --hash=sha256:573abb30e0e05bf31ed067d2f82500ecfdaec15627a59d63ea2d95714790f5c2 \
+    --hash=sha256:5b75aa69cb4d6f137b36f7eb2ace9280cfb60c55dc5f61c731fdf6f037f958a3 \
+    --hash=sha256:61332c87493b00091423e747ea78200659dc09bdf7fd69edd5e98cef5d3e9a8d \
+    --hash=sha256:805617228ba7e2cbbfb6c503858e626ab528ac2a32a04a2fe88ffaf6b02c32bc \
+    --hash=sha256:841ad858cff65c2c04bf93875e384ccb82b654574a6d7f30453a04f04af71342 \
+    --hash=sha256:89785bb2a1980c1bd87f0cb1517a71cde374776a5f150936b82580ae6ead44a1 \
+    --hash=sha256:8eb96e79b9f3dcadbad2a3891672f81cdcab7f95b27f28f1c67d75f045b6b4f1 \
+    --hash=sha256:974d8145f8ca354498005b5b981165b74a195abfae9a8129df3e56771961d595 \
+    --hash=sha256:9ddeb796389dadcd884c7eb07bd14ef12408aaae358f0e2ae24114d797eede30 \
+    --hash=sha256:a045f341a77b77e1c5de31e74e966537bba9f3c4099b35bf4c2e3939dd54cdab \
+    --hash=sha256:a0cffcbede75c059f535725c1680dfb17b6ba8753f0c74b14e6a9c68c29d7ea3 \
+    --hash=sha256:a761d9ccfc5e2ecd1bf05534eda382aa14c3e4f9205ba5b1684ecfe400716ef2 \
+    --hash=sha256:a7895f46d47671fa7ceec40f31fae721da51ad34bdca0bee83e38870b1f47ffd \
+    --hash=sha256:a9fa36448e6a3a1a9a2ba23c02012c43ed88905ec80163f2ffe2421c7192a5d7 \
+    --hash=sha256:ab29962927945d89d9b293eabd0d59aea28d887d4f3be6c22deaefbb938a7277 \
+    --hash=sha256:abbb49fb7dac584e5abc6636b7b2a7227111c4f771005853e7d25176daaf8453 \
+    --hash=sha256:ac4578ac281983f63b400f7fe6c101bedc10651650eef012be1ccffcbacf3697 \
+    --hash=sha256:adce39d67c0edf383647a3a007de0a45fd1b08dedaa5318404f1a73059c2512b \
+    --hash=sha256:ade08d343436a94e633db932e7e8407fe7de8083967962b46bdfc1b0ced39454 \
+    --hash=sha256:b2bdca22a27e35f16794cf585832e542123296b4687f9fd96822db6bae17bfc9 \
+    --hash=sha256:b2f926efda994cdf3c8d3fdb40b9962f86edbc4457e739277b961eced3d0b4c1 \
+    --hash=sha256:b457d6430833cee8e4b8e9b6f07aa1c161e5e0d52e118dc102c8f9bd7dd060d6 \
+    --hash=sha256:c414fc1ed8ee1dbd5da626cf3710c6013d3d27456651d156711fa24f24bd1291 \
+    --hash=sha256:cb76c1a154b83991a3cbbf0dfeb26ec2833ad56f95540b442c73950af2013750 \
+    --hash=sha256:dfd97abd83335045a913e3bcc4a09c0ceadbe66580cf573fe961f4a825efa699 \
+    --hash=sha256:e914a8cb05ce5c809dd0fe350cfbb4e881bde5e2a38dc04e3afe1b3e58bd158e \
+    --hash=sha256:ece6df05e2c41bd46776fbc712e0996f7c94e0d0543af1656956d150c4ca7c81 \
+    --hash=sha256:efa874e87e4a647fd2e4f514d5e91c7d493697127beb95e77d2f7561f6905bd9 \
+    --hash=sha256:f611e628ef06670df83fce17805c344710ca5cde01edfdc72751311da8585375
+    # via matplotlib
+cycler==0.12.1 \
+    --hash=sha256:85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30 \
+    --hash=sha256:88bb128f02ba341da8ef447245a9e138fae777f6a23943da4540077d3601eb1c
+    # via matplotlib
 dataclasses-json==0.6.7 \
     --hash=sha256:0dbf33f26c8d5305befd61b39d2b3414e8a407bedc2834dea9b8d642666fb40a \
     --hash=sha256:b6b3e528266ea45b9535223bc53ca645f5208833c29229e847b3f26a1cc55fc0
@@ -261,6 +325,11 @@ deprecated==1.2.18 \
     --hash=sha256:422b6f6d859da6f2ef57857761bfb392480502a64c3028ca9bbe86085d72115d \
     --hash=sha256:bd5011788200372a32418f888e326a09ff80d0214bd961147cfed01b5c018eec
     # via numcodecs
+descartes==1.1.0 \
+    --hash=sha256:135a502146af5ed6ff359975e2ebc5fa4b71b5432c355c2cafdc6dea1337035b \
+    --hash=sha256:4c62dc41109689d03e4b35de0a2bcbdeeb81047badc607c4415d5c753bd683af \
+    --hash=sha256:b7e412e7e6e294412f1d0f661f187babc970088c2456089e6801eebb043c2e1b
+    # via nuscenes-devkit
 docutils==0.22.4 \
     --hash=sha256:4db53b1fde9abecbb74d91230d32ab626d94f6badfc575d6db9194a49df29968 \
     --hash=sha256:d0013f540772d1420576855455d050a2180186c91c15779301ac2ccb3eeb68de
@@ -359,9 +428,65 @@ filelock==3.29.0 \
     --hash=sha256:69974355e960702e789734cb4871f884ea6fe50bd8404051a3530bc07809cf90 \
     --hash=sha256:96f5f6344709aa1572bbf631c640e4ebeeb519e08da902c39a001882f30ac258
     # via torch
+fire==0.7.1 \
+    --hash=sha256:3b208f05c736de98fb343310d090dcc4d8c78b2a89ea4f32b837c586270a9cbf \
+    --hash=sha256:e43fd8a5033a9001e7e2973bab96070694b9f12f2e0ecf96d4683971b5ab1882
+    # via nuscenes-devkit
 flatbuffers==25.12.19 \
     --hash=sha256:7634f50c427838bb021c2d66a3d1168e9d199b0607e6329399f04846d42e20b4
     # via tensorflow
+fonttools==4.56.0 \
+    --hash=sha256:003548eadd674175510773f73fb2060bb46adb77c94854af3e0cc5bc70260049 \
+    --hash=sha256:0073b62c3438cf0058488c002ea90489e8801d3a7af5ce5f7c05c105bee815c3 \
+    --hash=sha256:1088182f68c303b50ca4dc0c82d42083d176cba37af1937e1a976a31149d4d14 \
+    --hash=sha256:133bedb9a5c6376ad43e6518b7e2cd2f866a05b1998f14842631d5feb36b5786 \
+    --hash=sha256:14a3e3e6b211660db54ca1ef7006401e4a694e53ffd4553ab9bc87ead01d0f05 \
+    --hash=sha256:17f39313b649037f6c800209984a11fc256a6137cbe5487091c6c7187cae4685 \
+    --hash=sha256:193b86e9f769320bc98ffdb42accafb5d0c8c49bd62884f1c0702bc598b3f0a2 \
+    --hash=sha256:2d351275f73ebdd81dd5b09a8b8dac7a30f29a279d41e1c1192aedf1b6dced40 \
+    --hash=sha256:300c310bb725b2bdb4f5fc7e148e190bd69f01925c7ab437b9c0ca3e1c7cd9ba \
+    --hash=sha256:331954d002dbf5e704c7f3756028e21db07097c19722569983ba4d74df014000 \
+    --hash=sha256:38b947de71748bab150259ee05a775e8a0635891568e9fdb3cdd7d0e0004e62f \
+    --hash=sha256:3cf4f8d2a30b454ac682e12c61831dcb174950c406011418e739de592bbf8f76 \
+    --hash=sha256:3fd3fccb7b9adaaecfa79ad51b759f2123e1aba97f857936ce044d4f029abd71 \
+    --hash=sha256:442ad4122468d0e47d83bc59d0e91b474593a8c813839e1872e47c7a0cb53b10 \
+    --hash=sha256:47b5e4680002ae1756d3ae3b6114e20aaee6cc5c69d1e5911f5ffffd3ee46c6b \
+    --hash=sha256:53f5e9767978a4daf46f28e09dbeb7d010319924ae622f7b56174b777258e5ba \
+    --hash=sha256:62b4c6802fa28e14dba010e75190e0e6228513573f1eeae57b11aa1a39b7e5b1 \
+    --hash=sha256:62cc1253827d1e500fde9dbe981219fea4eb000fd63402283472d38e7d8aa1c6 \
+    --hash=sha256:654ac4583e2d7c62aebc6fc6a4c6736f078f50300e18aa105d87ce8925cfac31 \
+    --hash=sha256:661a8995d11e6e4914a44ca7d52d1286e2d9b154f685a4d1f69add8418961563 \
+    --hash=sha256:6c1d38642ca2dddc7ae992ef5d026e5061a84f10ff2b906be5680ab089f55bb8 \
+    --hash=sha256:6e81c1cc80c1d8bf071356cc3e0e25071fbba1c75afc48d41b26048980b3c771 \
+    --hash=sha256:705837eae384fe21cee5e5746fd4f4b2f06f87544fa60f60740007e0aa600311 \
+    --hash=sha256:7ef04bc7827adb7532be3d14462390dd71287644516af3f1e67f1e6ff9c6d6df \
+    --hash=sha256:86b2a1013ef7a64d2e94606632683f07712045ed86d937c11ef4dde97319c086 \
+    --hash=sha256:8d1613abd5af2f93c05867b3a3759a56e8bf97eb79b1da76b2bc10892f96ff16 \
+    --hash=sha256:965d0209e6dbdb9416100123b6709cb13f5232e2d52d17ed37f9df0cc31e2b35 \
+    --hash=sha256:96a4271f63a615bcb902b9f56de00ea225d6896052c49f20d0c91e9f43529a29 \
+    --hash=sha256:9d94449ad0a5f2a8bf5d2f8d71d65088aee48adbe45f3c5f8e00e3ad861ed81a \
+    --hash=sha256:9da650cb29bc098b8cfd15ef09009c914b35c7986c8fa9f08b51108b7bc393b4 \
+    --hash=sha256:a05d1f07eb0a7d755fbe01fee1fd255c3a4d3730130cf1bfefb682d18fd2fcea \
+    --hash=sha256:a114d1567e1a1586b7e9e7fc2ff686ca542a82769a296cef131e4c4af51e58f4 \
+    --hash=sha256:a1af375734018951c31c0737d04a9d5fd0a353a0253db5fbed2ccd44eac62d8c \
+    --hash=sha256:b23d30a2c0b992fb1c4f8ac9bfde44b5586d23457759b6cf9a787f1a35179ee0 \
+    --hash=sha256:bc871904a53a9d4d908673c6faa15689874af1c7c5ac403a8e12d967ebd0c0dc \
+    --hash=sha256:bce60f9a977c9d3d51de475af3f3581d9b36952e1f8fc19a1f2254f1dda7ce9c \
+    --hash=sha256:bd9825822e7bb243f285013e653f6741954d8147427aaa0324a862cdbf4cbf62 \
+    --hash=sha256:ca7962e8e5fc047cc4e59389959843aafbf7445b6c08c20d883e60ced46370a5 \
+    --hash=sha256:d0cb73ccf7f6d7ca8d0bc7ea8ac0a5b84969a41c56ac3ac3422a24df2680546f \
+    --hash=sha256:d54a45d30251f1d729e69e5b675f9a08b7da413391a1227781e2a297fa37f6d2 \
+    --hash=sha256:d6ca96d1b61a707ba01a43318c9c40aaf11a5a568d1e61146fafa6ab20890793 \
+    --hash=sha256:d6f195c14c01bd057bc9b4f70756b510e009c83c5ea67b25ced3e2c38e6ee6e9 \
+    --hash=sha256:e2cad98c94833465bcf28f51c248aaf07ca022efc6a3eba750ad9c1e0256d278 \
+    --hash=sha256:e2e993e8db36306cc3f1734edc8ea67906c55f98683d6fd34c3fc5593fdbba4c \
+    --hash=sha256:e9270505a19361e81eecdbc2c251ad1e1a9a9c2ad75fa022ccdee533f55535dc \
+    --hash=sha256:f20e2c0dfab82983a90f3d00703ac0960412036153e5023eed2b4641d7d5e692 \
+    --hash=sha256:f36a0868f47b7566237640c026c65a86d09a3d9ca5df1cd039e30a1da73098a0 \
+    --hash=sha256:f59746f7953f69cc3290ce2f971ab01056e55ddd0fb8b792c31a8acd7fee2d28 \
+    --hash=sha256:fa760e5fe8b50cbc2d71884a1eff2ed2b95a005f02dda2fa431560db0ddd927f \
+    --hash=sha256:ffda9b8cd9cb8b301cae2602ec62375b59e2e2108a117746f12215145e3f786c
+    # via matplotlib
 fsspec==2025.10.0 \
     --hash=sha256:7c7712353ae7d875407f97715f0e1ffcc21e33d5b24556cb1e090ae9409ec61d \
     --hash=sha256:b6789427626f068f9a83ca4e8a3cc050850b6c0f71f99ddb4f542b8266a26a59
@@ -519,6 +644,10 @@ jinja2==3.1.5 \
     #   nbsphinx
     #   sphinx
     #   torch
+joblib==1.5.3 \
+    --hash=sha256:5fc3c5039fc5ca8c0276333a188bbd59d6b7ab37fe6632daa76bc7f9ec18e713 \
+    --hash=sha256:8561a3269e6801106863fd0d6d84bb737be9e7631e33aaed3fb9ce5953688da3
+    # via scikit-learn
 jsonschema==4.23.0 \
     --hash=sha256:d71497fef26351a33265337fa77ffeb82423f3ea21283cd9467bb03999266bc4 \
     --hash=sha256:fbadb6f8b144a8f8cf9f0b89ba94501d143e50411a1278633f56a7acf7fd5566
@@ -549,6 +678,88 @@ keras==3.14.1 \
     --hash=sha256:ebd2c14d2af3c9de18083604d408483996407fc7d2f9ebd1d565961f96608c29 \
     --hash=sha256:ef479173102ad29db89b53c232efdc3fb5ad57c28bc27ead59f3e78a1eecd05b
     # via tensorflow
+kiwisolver==1.4.8 \
+    --hash=sha256:01c3d31902c7db5fb6182832713d3b4122ad9317c2c5877d0539227d96bb2e50 \
+    --hash=sha256:034d2c891f76bd3edbdb3ea11140d8510dca675443da7304205a2eaa45d8334c \
+    --hash=sha256:085940635c62697391baafaaeabdf3dd7a6c3643577dde337f4d66eba021b2b8 \
+    --hash=sha256:08e77738ed7538f036cd1170cbed942ef749137b1311fa2bbe2a7fda2f6bf3cc \
+    --hash=sha256:111793b232842991be367ed828076b03d96202c19221b5ebab421ce8bcad016f \
+    --hash=sha256:11e1022b524bd48ae56c9b4f9296bce77e15a2e42a502cceba602f804b32bb79 \
+    --hash=sha256:151dffc4865e5fe6dafce5480fab84f950d14566c480c08a53c663a0020504b6 \
+    --hash=sha256:16523b40aab60426ffdebe33ac374457cf62863e330a90a0383639ce14bf44b2 \
+    --hash=sha256:1732e065704b47c9afca7ffa272f845300a4eb959276bf6970dc07265e73b605 \
+    --hash=sha256:1c8ceb754339793c24aee1c9fb2485b5b1f5bb1c2c214ff13368431e51fc9a09 \
+    --hash=sha256:23454ff084b07ac54ca8be535f4174170c1094a4cff78fbae4f73a4bcc0d4dab \
+    --hash=sha256:23d5f023bdc8c7e54eb65f03ca5d5bb25b601eac4d7f1a042888a1f45237987e \
+    --hash=sha256:257af1622860e51b1a9d0ce387bf5c2c4f36a90594cb9514f55b074bcc787cfc \
+    --hash=sha256:286b18e86682fd2217a48fc6be6b0f20c1d0ed10958d8dc53453ad58d7be0bf8 \
+    --hash=sha256:291331973c64bb9cce50bbe871fb2e675c4331dab4f31abe89f175ad7679a4d7 \
+    --hash=sha256:2f0121b07b356a22fb0414cec4666bbe36fd6d0d759db3d37228f496ed67c880 \
+    --hash=sha256:3452046c37c7692bd52b0e752b87954ef86ee2224e624ef7ce6cb21e8c41cc1b \
+    --hash=sha256:34d142fba9c464bc3bbfeff15c96eab0e7310343d6aefb62a79d51421fcc5f1b \
+    --hash=sha256:369b75d40abedc1da2c1f4de13f3482cb99e3237b38726710f4a793432b1c5ff \
+    --hash=sha256:36dbbfd34838500a31f52c9786990d00150860e46cd5041386f217101350f0d3 \
+    --hash=sha256:370fd2df41660ed4e26b8c9d6bbcad668fbe2560462cba151a721d49e5b6628c \
+    --hash=sha256:3a96c0e790ee875d65e340ab383700e2b4891677b7fcd30a699146f9384a2bb0 \
+    --hash=sha256:3b9b4d2892fefc886f30301cdd80debd8bb01ecdf165a449eb6e78f79f0fabd6 \
+    --hash=sha256:3cd3bc628b25f74aedc6d374d5babf0166a92ff1317f46267f12d2ed54bc1d30 \
+    --hash=sha256:3ddc373e0eef45b59197de815b1b28ef89ae3955e7722cc9710fb91cd77b7f47 \
+    --hash=sha256:4191ee8dfd0be1c3666ccbac178c5a05d5f8d689bbe3fc92f3c4abec817f8fe0 \
+    --hash=sha256:54a62808ac74b5e55a04a408cda6156f986cefbcf0ada13572696b507cc92fa1 \
+    --hash=sha256:577facaa411c10421314598b50413aa1ebcf5126f704f1e5d72d7e4e9f020d90 \
+    --hash=sha256:641f2ddf9358c80faa22e22eb4c9f54bd3f0e442e038728f500e3b978d00aa7d \
+    --hash=sha256:65ea09a5a3faadd59c2ce96dc7bf0f364986a315949dc6374f04396b0d60e09b \
+    --hash=sha256:68269e60ee4929893aad82666821aaacbd455284124817af45c11e50a4b42e3c \
+    --hash=sha256:69b5637c3f316cab1ec1c9a12b8c5f4750a4c4b71af9157645bf32830e39c03a \
+    --hash=sha256:7506488470f41169b86d8c9aeff587293f530a23a23a49d6bc64dab66bedc71e \
+    --hash=sha256:768cade2c2df13db52475bd28d3a3fac8c9eff04b0e9e2fda0f3760f20b3f7fc \
+    --hash=sha256:77e6f57a20b9bd4e1e2cedda4d0b986ebd0216236f0106e55c28aea3d3d69b16 \
+    --hash=sha256:782bb86f245ec18009890e7cb8d13a5ef54dcf2ebe18ed65f795e635a96a1c6a \
+    --hash=sha256:7a3ad337add5148cf51ce0b55642dc551c0b9d6248458a757f98796ca7348712 \
+    --hash=sha256:7cd2785b9391f2873ad46088ed7599a6a71e762e1ea33e87514b1a441ed1da1c \
+    --hash=sha256:7e9a60b50fe8b2ec6f448fe8d81b07e40141bfced7f896309df271a0b92f80f3 \
+    --hash=sha256:84a2f830d42707de1d191b9490ac186bf7997a9495d4e9072210a1296345f7dc \
+    --hash=sha256:856b269c4d28a5c0d5e6c1955ec36ebfd1651ac00e1ce0afa3e28da95293b561 \
+    --hash=sha256:858416b7fb777a53f0c59ca08190ce24e9abbd3cffa18886a5781b8e3e26f65d \
+    --hash=sha256:87b287251ad6488e95b4f0b4a79a6d04d3ea35fde6340eb38fbd1ca9cd35bbbc \
+    --hash=sha256:88c6f252f6816a73b1f8c904f7bbe02fd67c09a69f7cb8a0eecdbf5ce78e63db \
+    --hash=sha256:893f5525bb92d3d735878ec00f781b2de998333659507d29ea4466208df37bed \
+    --hash=sha256:89c107041f7b27844179ea9c85d6da275aa55ecf28413e87624d033cf1f6b751 \
+    --hash=sha256:918139571133f366e8362fa4a297aeba86c7816b7ecf0bc79168080e2bd79957 \
+    --hash=sha256:99cea8b9dd34ff80c521aef46a1dddb0dcc0283cf18bde6d756f1e6f31772165 \
+    --hash=sha256:a17b7c4f5b2c51bb68ed379defd608a03954a1845dfed7cc0117f1cc8a9b7fd2 \
+    --hash=sha256:a3c44cb68861de93f0c4a8175fbaa691f0aa22550c331fefef02b618a9dcb476 \
+    --hash=sha256:a4d3601908c560bdf880f07d94f31d734afd1bb71e96585cace0e38ef44c6d84 \
+    --hash=sha256:a5ce1e481a74b44dd5e92ff03ea0cb371ae7a0268318e202be06c8f04f4f1246 \
+    --hash=sha256:a66f60f8d0c87ab7f59b6fb80e642ebb29fec354a4dfad687ca4092ae69d04f4 \
+    --hash=sha256:b21dbe165081142b1232a240fc6383fd32cdd877ca6cc89eab93e5f5883e1c25 \
+    --hash=sha256:b47a465040146981dc9db8647981b8cb96366fbc8d452b031e4f8fdffec3f26d \
+    --hash=sha256:b5773efa2be9eb9fcf5415ea3ab70fc785d598729fd6057bea38d539ead28271 \
+    --hash=sha256:b83dc6769ddbc57613280118fb4ce3cd08899cc3369f7d0e0fab518a7cf37fdb \
+    --hash=sha256:bade438f86e21d91e0cf5dd7c0ed00cda0f77c8c1616bd83f9fc157fa6760d31 \
+    --hash=sha256:bcb1ebc3547619c3b58a39e2448af089ea2ef44b37988caf432447374941574e \
+    --hash=sha256:be4816dc51c8a471749d664161b434912eee82f2ea66bd7628bd14583a833e85 \
+    --hash=sha256:c07b29089b7ba090b6f1a669f1411f27221c3662b3a1b7010e67b59bb5a6f10b \
+    --hash=sha256:c2b9a96e0f326205af81a15718a9073328df1173a2619a68553decb7097fd5d7 \
+    --hash=sha256:c5020c83e8553f770cb3b5fc13faac40f17e0b205bd237aebd21d53d733adb03 \
+    --hash=sha256:c72941acb7b67138f35b879bbe85be0f6c6a70cab78fe3ef6db9c024d9223e5b \
+    --hash=sha256:c8bf637892dc6e6aad2bc6d4d69d08764166e5e3f69d469e55427b6ac001b19d \
+    --hash=sha256:cc978a80a0db3a66d25767b03688f1147a69e6237175c0f4ffffaaedf744055a \
+    --hash=sha256:ce2cf1e5688edcb727fdf7cd1bbd0b6416758996826a8be1d958f91880d0809d \
+    --hash=sha256:d47b28d1dfe0793d5e96bce90835e17edf9a499b53969b03c6c47ea5985844c3 \
+    --hash=sha256:d47cfb2650f0e103d4bf68b0b5804c68da97272c84bb12850d877a95c056bd67 \
+    --hash=sha256:d5536185fce131780ebd809f8e623bf4030ce1b161353166c49a3c74c287897f \
+    --hash=sha256:d561d2d8883e0819445cfe58d7ddd673e4015c3c57261d7bdcd3710d0d14005c \
+    --hash=sha256:d6af5e8815fd02997cb6ad9bbed0ee1e60014438ee1a5c2444c96f87b8843502 \
+    --hash=sha256:d6d6bd87df62c27d4185de7c511c6248040afae67028a8a22012b010bc7ad062 \
+    --hash=sha256:dace81d28c787956bfbfbbfd72fdcef014f37d9b48830829e488fdb32b49d954 \
+    --hash=sha256:e063ef9f89885a1d68dd8b2e18f5ead48653176d10a0e324e3b0030e3a69adeb \
+    --hash=sha256:e7a019419b7b510f0f7c9dceff8c5eae2392037eae483a7f9162625233802b0a \
+    --hash=sha256:eaa973f1e05131de5ff3569bbba7f5fd07ea0595d3870ed4a526d486fe57fa1b \
+    --hash=sha256:eb158fe28ca0c29f2260cca8c43005329ad58452c36f0edf298204de32a9a3ed \
+    --hash=sha256:ed33ca2002a779a2e20eeb06aea7721b6e47f2d4b8a8ece979d8ba9e2a167e34 \
+    --hash=sha256:fc2ace710ba7c1dfd1a3b42530b62b9ceed115f19a1656adefce7b1782a37794
+    # via matplotlib
 libclang==18.1.1 \
     --hash=sha256:0b2e143f0fac830156feb56f9231ff8338c20aecfe72b4ffe96f19e5a1dbb69a \
     --hash=sha256:3f0e1f49f04d3cd198985fea0511576b0aee16f9ff0e0f0cad7f9c57ec3c20e8 \
@@ -885,6 +1096,44 @@ marshmallow==3.26.1 \
     --hash=sha256:3350409f20a70a7e4e11a27661187b77cdcaeb20abca41c1454fe33636bea09c \
     --hash=sha256:e6d8affb6cb61d39d26402096dc0aee12d5a26d490a121f118d2e81dc0719dc6
     # via dataclasses-json
+matplotlib==3.10.0 \
+    --hash=sha256:01d2b19f13aeec2e759414d3bfe19ddfb16b13a1250add08d46d5ff6f9be83c6 \
+    --hash=sha256:12eaf48463b472c3c0f8dbacdbf906e573013df81a0ab82f0616ea4b11281908 \
+    --hash=sha256:2c5829a5a1dd5a71f0e31e6e8bb449bc0ee9dbfb05ad28fc0c6b55101b3a4be6 \
+    --hash=sha256:2fbbabc82fde51391c4da5006f965e36d86d95f6ee83fb594b279564a4c5d0d2 \
+    --hash=sha256:3547d153d70233a8496859097ef0312212e2689cdf8d7ed764441c77604095ae \
+    --hash=sha256:359f87baedb1f836ce307f0e850d12bb5f1936f70d035561f90d41d305fdacea \
+    --hash=sha256:3b427392354d10975c1d0f4ee18aa5844640b512d5311ef32efd4dd7db106ede \
+    --hash=sha256:4659665bc7c9b58f8c00317c3c2a299f7f258eeae5a5d56b4c64226fca2f7c59 \
+    --hash=sha256:4673ff67a36152c48ddeaf1135e74ce0d4bce1bbf836ae40ed39c29edf7e2765 \
+    --hash=sha256:503feb23bd8c8acc75541548a1d709c059b7184cde26314896e10a9f14df5f12 \
+    --hash=sha256:5439f4c5a3e2e8eab18e2f8c3ef929772fd5641876db71f08127eed95ab64683 \
+    --hash=sha256:5cdbaf909887373c3e094b0318d7ff230b2ad9dcb64da7ade654182872ab2593 \
+    --hash=sha256:5e6c6461e1fc63df30bf6f80f0b93f5b6784299f721bc28530477acd51bfc3d1 \
+    --hash=sha256:5fd41b0ec7ee45cd960a8e71aea7c946a28a0b8a4dcee47d2856b2af051f334c \
+    --hash=sha256:607b16c8a73943df110f99ee2e940b8a1cbf9714b65307c040d422558397dac5 \
+    --hash=sha256:7e8632baebb058555ac0cde75db885c61f1212e47723d63921879806b40bec6a \
+    --hash=sha256:81713dd0d103b379de4516b861d964b1d789a144103277769238c732229d7f03 \
+    --hash=sha256:845d96568ec873be63f25fa80e9e7fae4be854a66a7e2f0c8ccc99e94a8bd4ef \
+    --hash=sha256:95b710fea129c76d30be72c3b38f330269363fbc6e570a5dd43580487380b5ff \
+    --hash=sha256:96f2886f5c1e466f21cc41b70c5a0cd47bfa0015eb2d5793c88ebce658600e25 \
+    --hash=sha256:994c07b9d9fe8d25951e3202a68c17900679274dadfc1248738dcfa1bd40d7f3 \
+    --hash=sha256:9ade1003376731a971e398cc4ef38bb83ee8caf0aee46ac6daa4b0506db1fd06 \
+    --hash=sha256:9b0558bae37f154fffda54d779a592bc97ca8b4701f1c710055b609a3bac44c8 \
+    --hash=sha256:a2a43cbefe22d653ab34bb55d42384ed30f611bcbdea1f8d7f431011a2e1c62e \
+    --hash=sha256:a994f29e968ca002b50982b27168addfd65f0105610b6be7fa515ca4b5307c95 \
+    --hash=sha256:ad2e15300530c1a94c63cfa546e3b7864bd18ea2901317bae8bbf06a5ade6dcf \
+    --hash=sha256:ae80dc3a4add4665cf2faa90138384a7ffe2a4e37c58d83e115b54287c4f06ef \
+    --hash=sha256:b886d02a581b96704c9d1ffe55709e49b4d2d52709ccebc4be42db856e511278 \
+    --hash=sha256:c40ba2eb08b3f5de88152c2333c58cee7edcead0a2a0d60fcafa116b17117adc \
+    --hash=sha256:c55b20591ced744aa04e8c3e4b7543ea4d650b6c3c4b208c08a05b4010e8b442 \
+    --hash=sha256:c58a9622d5dbeb668f407f35f4e6bfac34bb9ecdcc81680c04d0258169747997 \
+    --hash=sha256:d44cb942af1693cced2604c33a9abcef6205601c445f6d0dc531d813af8a2f5a \
+    --hash=sha256:d907fddb39f923d011875452ff1eca29a9e7f21722b873e90db32e5d8ddff12e \
+    --hash=sha256:fd44fc75522f58612ec4a33958a7e5552562b7705b42ef1b4f8c0818e304a363
+    # via
+    #   descartes
+    #   nuscenes-devkit
 matplotlib-inline==0.2.1 \
     --hash=sha256:d56ce5156ba6085e00a9d54fead6ed29a9c47e215cd1bba2e976ef39f5710a76 \
     --hash=sha256:e1ee949c340d771fc39e241ea75683deb94762c8fa5f2927ec57c83c4dffa9fe
@@ -1094,18 +1343,24 @@ numpy==1.26.4 \
     #   -r deps/pip/requirements_pai.in
     #   -r deps/pip/requirements_tools.in
     #   -r deps/pip/requirements_waymo.in
+    #   contourpy
     #   embreex
     #   h5py
     #   imageio
     #   keras
     #   manifold3d
     #   mapbox-earcut
+    #   matplotlib
     #   ml-dtypes
     #   numcodecs
+    #   nuscenes-devkit
     #   opencv-python-headless
     #   pandas
     #   point-cloud-utils
+    #   pycocotools
     #   pycollada
+    #   pyquaternion
+    #   scikit-learn
     #   scipy
     #   shapely
     #   tensorboard
@@ -1115,6 +1370,9 @@ numpy==1.26.4 \
     #   viser
     #   yourdfpy
     #   zarr
+nuscenes-devkit==1.2.0 \
+    --hash=sha256:76cee0e7f96ec96d6269ee3acb4ff69e8ac2f9413e974d9eb542233ea7479bf1
+    # via -r deps/pip/requirements_nuscenes.in
 nvidia-cublas-cu12==12.8.3.14 \
     --hash=sha256:3f0e05e7293598cf61933258b73e66a160c27d59c4422670bf0b79348c04be44 \
     --hash=sha256:93a4e0e386cc7f6e56c822531396de8170ed17068a1e18f987574895044cd8c3 \
@@ -1206,6 +1464,7 @@ opencv-python-headless==4.11.0.86 \
     # via
     #   -r deps/pip/requirements_tests.in
     #   -r deps/pip/requirements_tools.in
+    #   nuscenes-devkit
 opt-einsum==3.4.0 \
     --hash=sha256:69bb92469f86a1565195ece4ac0323943e83477171b91d24c35afe028a90d7cd \
     --hash=sha256:96ca72f1b886d148241348783498194c577fa30a8faac108586b14f1ba4473ac
@@ -1298,6 +1557,7 @@ packaging==24.2 \
     # via
     #   keras
     #   marshmallow
+    #   matplotlib
     #   nbconvert
     #   pytest
     #   sphinx
@@ -1356,7 +1616,9 @@ pandocfilters==1.5.1 \
 parameterized==0.9.0 \
     --hash=sha256:4e0758e3d41bea3bbd05ec14fc2c24736723f243b28d702081aef438c9372b1b \
     --hash=sha256:7fc905272cefa4f364c1a3429cbbe9c0f98b793988efb5bf90aac80f08db09b1
-    # via -r deps/pip/requirements_tests.in
+    # via
+    #   -r deps/pip/requirements_tests.in
+    #   nuscenes-devkit
 parso==0.8.6 \
     --hash=sha256:2b9a0332696df97d454fa67b81618fd69c35a7b90327cbe6ba5c92d2c68a7bfd \
     --hash=sha256:2c549f800b70a5c4952197248825584cb00f033b29c692671d3bf08bf380baff
@@ -1467,6 +1729,8 @@ pillow==12.2.0 \
     #   -r deps/pip/requirements_ncore.in
     #   -r deps/pip/requirements_pai.in
     #   imageio
+    #   matplotlib
+    #   nuscenes-devkit
     #   tensorboard
     #   trimesh
 platformdirs==4.3.6 \
@@ -1590,6 +1854,51 @@ pyarrow==23.0.1 \
     --hash=sha256:fa8e51cb04b9f8c9c5ace6bab63af9a1f88d35c0d6cbf53e8c17c098552285e1 \
     --hash=sha256:fed7020203e9ef273360b9e45be52a2a47d3103caf156a30ace5247ffb51bdbd
     # via -r deps/pip/requirements_pai.in
+pycocotools==2.0.11 \
+    --hash=sha256:04480330df5013f6edd94891a0ee8294274185f1b5093d1b0f23d51778f0c0e9 \
+    --hash=sha256:08c79789fd79e801ae4ecfcfeec32b31e36254e7a2b4019af28c104975d5e730 \
+    --hash=sha256:1192de413a23b4b94199197e8f8dbe1277cc24e6e9847bee6a71be3d8e543963 \
+    --hash=sha256:18ba75ff58cedb33a85ce2c18f1452f1fe20c9dd59925eec5300b2bf6205dbe1 \
+    --hash=sha256:34254d76da85576fcaf5c1f3aa9aae16b8cb15418334ba4283b800796bd1993d \
+    --hash=sha256:368244f30eb8d6cae7003aa2c0831fbdf0153664a32859ec7fbceea52bfb6878 \
+    --hash=sha256:484d33515353186aadba9e2a290d81b107275cdb9565084e31a5568a52a0b120 \
+    --hash=sha256:4fc9889e819452b9c142036e1eabac8a13a8bd552d8beba299a57e0da6bfa1ec \
+    --hash=sha256:63026e11a56211058d0e84e8263f74cbccd5e786fac18d83fd221ecb9819fcc7 \
+    --hash=sha256:693417797f0377fd094eb815c0a1e7d1c3c0251b71e3b3779fce3b3cf24793c5 \
+    --hash=sha256:6a2f4f036f5bfdaf8c9625279051e921721fc9d27f92c97a3f3355a07ba38513 \
+    --hash=sha256:78bae4a9de9d34c4759754a848dfb3306f9ef1c2fcb12164ffbd3d013d008321 \
+    --hash=sha256:7cd4cdfd2c676f30838aa0b1047441892fb4f97d70bf3df480bcc7a18a64d7d4 \
+    --hash=sha256:7fd4121766cc057133534679c0ec3f9023dbd96e9b31cf95c86a069ebdac2b65 \
+    --hash=sha256:81bdceebb4c64e9265213e2d733808a12f9c18dfb14457323cc6b9af07fa0e61 \
+    --hash=sha256:83d896f4310379849dfcfa7893afb0ff21f4f3cdb04ab3f61b05dd98953dd0ad \
+    --hash=sha256:87af87b8d06d5b852a885a319d9362dca3bed9f8bbcc3feb6513acb1f88ea242 \
+    --hash=sha256:89e853425018e2c2920ee0f2112cf7c140a1dcf5f4f49abd9c2da112c3e0f4b3 \
+    --hash=sha256:8cedb8ccb97ffe9ed2c8c259234fa69f4f1e8665afe3a02caf93f6ef2952c07f \
+    --hash=sha256:8e159232adae3aef6b4e2d37b008bff107b26e9ed3b48e70ea6482302834bd34 \
+    --hash=sha256:9dc8b388984c72aa84b1a68933f196ce71ab114c59232d0eab20c97cc1300875 \
+    --hash=sha256:a1c05f91ccc658dfe01325267209c4b435da1722c93eeb5749fabc1d087b6882 \
+    --hash=sha256:a2b018497ec198ffc737dd7e6306a2e69999779ca619a9e12950e4506e410c3e \
+    --hash=sha256:a2e9634bc7cadfb01c88e0b98589aaf0bd12983c7927bde93f19c0103e5441f4 \
+    --hash=sha256:a6b13baf6bfcf881b6d6ac6e23c776f87a68304cd86e53d1d6b9afa31e363c4e \
+    --hash=sha256:a82d1c9ed83f75da0b3f244f2a3cf559351a283307bd9b79a4ee2b93ab3231dd \
+    --hash=sha256:ac8aa17263e6489aa521f9fa91e959dfe0ea3a5519fde2cbf547312cdce7559e \
+    --hash=sha256:b6a07071c441d0f5e480a8f287106191582e40289d4e242dfe684e0c8a751088 \
+    --hash=sha256:bd7a1e19ef56a828a94bace673372071d334a9232cd32ae3cd48845a04d45c4f \
+    --hash=sha256:c230f5e7b14bd19085217b4f40bba81bf14a182b150b8e9fab1c15d504ade343 \
+    --hash=sha256:c3546b93b39943347c4f5b0694b5824105cbe2174098a416bcad4acd9c21e957 \
+    --hash=sha256:ca9f120f719ec405ad0c74ccfdb8402b0c37bd5f88ab5b6482a0de2efd5a36f4 \
+    --hash=sha256:d6b88557166794a24acd03f50296f2b95adf3e4206b4b8995e6bdca925c9cb72 \
+    --hash=sha256:e21311ea71f85591680d8992858e2d44a2a156dc3b2bf1c5c901c4a19348177b \
+    --hash=sha256:e40a3a898c6e5340b8d70cf7984868b9bff8c3d80187de9a3b661d504d665978 \
+    --hash=sha256:e72527fdc00985d29dbe31d17b19cd2d16fbad7b01e974c567b593d5844de710 \
+    --hash=sha256:eba35d6e06caaea28ce65a4b92e4343ecbb9abf4915f7ef7fca989b80839111c \
+    --hash=sha256:eebd723503a2eb2c8b285f56ea3be1d9f3875cd7c40d945358a428db94f14015 \
+    --hash=sha256:efd1694b2075f2f10c5828f10f6e6c4e44368841fd07dae385c3aa015c8e25f9 \
+    --hash=sha256:f78cbb1a32d061fcad4bdba083de70a39a21c1c3d9235a3f77d8f007541ec5ef \
+    --hash=sha256:f7eb43b79448476b094240450420b7425d06e297880144b8ea6f01e9b4340e43 \
+    --hash=sha256:fd72b9734e6084b217c1fc3945bfd4ec05bdc75a44e4f0c461a91442bb804973 \
+    --hash=sha256:ffe806ce535f5996445188f9a35643791dc54beabc61bd81e2b03367356d604f
+    # via nuscenes-devkit
 pycollada==0.9.3 \
     --hash=sha256:636e6496f60987586db82455ea7bbd9ade775e8181c6590c83b698b6cd53a9f5 \
     --hash=sha256:c34d6dcf0fe2eba5896f71c96d37a1c0fe1a61f08440fa0cfcec3dc2895d3302
@@ -1622,6 +1931,17 @@ pynvvideocodec==2.1.0 \
     --hash=sha256:e778af3319a759c1728065e9e4682f591c44dedbbc90c1ca71d025dce65d6a58 \
     --hash=sha256:ee5d2dc56ac5ca8d223fa6fe8f57c67faf5ee1c9360216d9194cfbcd3f9d3948
     # via -r deps/pip/requirements_pai.in
+pyparsing==3.2.1 \
+    --hash=sha256:506ff4f4386c4cec0590ec19e6302d3aedb992fdc02c761e90416f158dacf8e1 \
+    --hash=sha256:61980854fd66de3a90028d679a954d5f2623e83144b5afe5ee86f43d762e5f0a
+    # via matplotlib
+pyquaternion==0.9.9 \
+    --hash=sha256:b1f61af219cb2fe966b5fb79a192124f2e63a3f7a777ac3cadf2957b1a81bea8 \
+    --hash=sha256:d0eb69219ca99bfcbc25c1e2c4f82e58c61dce3e907e929f13c5f3615e4b6518 \
+    --hash=sha256:e65f6e3f7b1fdf1a9e23f82434334a1ae84f14223eee835190cd2e841f8172ec
+    # via
+    #   -r deps/pip/requirements_nuscenes.in
+    #   nuscenes-devkit
 pytest==8.3.4 \
     --hash=sha256:50e16d954148559c9a74109af1eaf0c945ba2d8f30f0a3d3335edde19788b6f6 \
     --hash=sha256:965370d062bce11e73868e0335abac31b4d3de0e82f4007408d242b4f8610761
@@ -1631,6 +1951,7 @@ python-dateutil==2.9.0.post0 \
     --hash=sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427
     # via
     #   jupyter-client
+    #   matplotlib
     #   pandas
     #   pycollada
 pytz==2025.1 \
@@ -1895,6 +2216,45 @@ rtree==1.4.1 \
     --hash=sha256:efe125f416fd27150197ab8521158662943a40f87acab8028a1aac4ad667a489 \
     --hash=sha256:f155bc8d6bac9dcd383481dee8c130947a4866db1d16cb6dff442329a038a0dc
     # via trimesh
+scikit-learn==1.8.0 \
+    --hash=sha256:00d6f1d66fbcf4eba6e356e1420d33cc06c70a45bb1363cd6f6a8e4ebbbdece2 \
+    --hash=sha256:0d6ae97234d5d7079dc0040990a6f7aeb97cb7fa7e8945f1999a429b23569e0a \
+    --hash=sha256:146b4d36f800c013d267b29168813f7a03a43ecd2895d04861f1240b564421da \
+    --hash=sha256:15fc3b5d19cc2be65404786857f2e13c70c83dd4782676dd6814e3b89dc8f5b9 \
+    --hash=sha256:2838551e011a64e3053ad7618dda9310175f7515f1742fa2d756f7c874c05961 \
+    --hash=sha256:29ffc74089f3d5e87dfca4c2c8450f88bdc61b0fc6ed5d267f3988f19a1309f6 \
+    --hash=sha256:2de443b9373b3b615aec1bb57f9baa6bb3a9bd093f1269ba95c17d870422b271 \
+    --hash=sha256:35c007dedb2ffe38fe3ee7d201ebac4a2deccd2408e8621d53067733e3c74809 \
+    --hash=sha256:3bad7565bc9cf37ce19a7c0d107742b320c1285df7aab1a6e2d28780df167242 \
+    --hash=sha256:4496bb2cf7a43ce1a2d7524a79e40bc5da45cf598dbf9545b7e8316ccba47bb4 \
+    --hash=sha256:4511be56637e46c25721e83d1a9cea9614e7badc7040c4d573d75fbe257d6fd7 \
+    --hash=sha256:5025ce924beccb28298246e589c691fe1b8c1c96507e6d27d12c5fadd85bfd76 \
+    --hash=sha256:56079a99c20d230e873ea40753102102734c5953366972a71d5cb39a32bc40c6 \
+    --hash=sha256:5e30adb87f0cc81c7690a84f7932dd66be5bac57cfe16b91cb9151683a4a2d3b \
+    --hash=sha256:5fb63362b5a7ddab88e52b6dbb47dac3fd7dafeee740dc6c8d8a446ddedade8e \
+    --hash=sha256:6b595b07a03069a2b1740dc08c2299993850ea81cce4fe19b2421e0c970de6b7 \
+    --hash=sha256:72358cce49465d140cc4e7792015bb1f0296a9742d5622c67e31399b75468b9e \
+    --hash=sha256:74b66d8689d52ed04c271e1329f0c61635bcaf5b926db9b12d58914cdc01fe57 \
+    --hash=sha256:7cc267b6108f0a1499a734167282c00c4ebf61328566b55ef262d48e9849c735 \
+    --hash=sha256:80832434a6cc114f5219211eec13dcbc16c2bac0e31ef64c6d346cde3cf054cb \
+    --hash=sha256:8c497fff237d7b4e07e9ef1a640887fa4fb765647f86fbe00f969ff6280ce2bb \
+    --hash=sha256:8fdf95767f989b0cfedb85f7ed8ca215d4be728031f56ff5a519ee1e3276dc2e \
+    --hash=sha256:9bccbb3b40e3de10351f8f5068e105d0f4083b1a65fa07b6634fbc401a6287fd \
+    --hash=sha256:a0bcfe4d0d14aec44921545fd2af2338c7471de9cb701f1da4c9d85906ab847a \
+    --hash=sha256:a69525355a641bf8ef136a7fa447672fb54fe8d60cab5538d9eb7c6438543fb9 \
+    --hash=sha256:ada8121bcb4dac28d930febc791a69f7cb1673c8495e5eee274190b73a4559c1 \
+    --hash=sha256:bf97c10a3f5a7543f9b88cbf488d33d175e9146115a451ae34568597ba33dcde \
+    --hash=sha256:c22a2da7a198c28dd1a6e1136f19c830beab7fdca5b3e5c8bba8394f8a5c45b3 \
+    --hash=sha256:c2656924ec73e5939c76ac4c8b026fc203b83d8900362eb2599d8aee80e4880f \
+    --hash=sha256:c57b1b610bd1f40ba43970e11ce62821c2e6569e4d74023db19c6b26f246cb3b \
+    --hash=sha256:eddde82a035681427cbedded4e6eff5e57fa59216c2e3e90b10b19ab1d0a65c3 \
+    --hash=sha256:edec98c5e7c128328124a029bceb09eda2d526997780fef8d65e9a69eead963e \
+    --hash=sha256:ee787491dbfe082d9c3013f01f5991658b0f38aa8177e4cd4bf434c58f551702 \
+    --hash=sha256:f28dd15c6bb0b66ba09728cf09fd8736c304be29409bd8445a080c1280619e8c \
+    --hash=sha256:f984ca4b14914e6b4094c5d52a32ea16b49832c03bd17a110f004db3c223e8e1 \
+    --hash=sha256:fb65db5d7531bccf3a4f6bec3462223bea71384e2cda41da0f10b7c292b9e7c4 \
+    --hash=sha256:fe1c011a640a9f0791146011dfd3c7d9669785f9fed2b2a5f9e207536cf5c2fd
+    # via nuscenes-devkit
 scipy==1.15.2 \
     --hash=sha256:01edfac9f0798ad6b46d9c4c9ca0e0ad23dbf0b1eb70e96adb9fa7f525eff0bf \
     --hash=sha256:03205d57a28e18dfd39f0377d5002725bf1f19a46f444108c29bdb246b6c8a11 \
@@ -1948,7 +2308,9 @@ scipy==1.15.2 \
     #   -r deps/pip/requirements_pai.in
     #   -r deps/pip/requirements_tools.in
     #   -r deps/pip/requirements_waymo.in
+    #   nuscenes-devkit
     #   point-cloud-utils
+    #   scikit-learn
     #   trimesh
 setuptools==75.8.0 \
     --hash=sha256:c5afc8f407c626b8313a86e10311dd3f661c6cd9c09d4bf8c15c0e11f9f2b0e6 \
@@ -1957,65 +2319,52 @@ setuptools==75.8.0 \
     #   tensorboard
     #   tensorflow
     #   triton
-shapely==2.1.2 \
-    --hash=sha256:0036ac886e0923417932c2e6369b6c52e38e0ff5d9120b90eef5cd9a5fc5cae9 \
-    --hash=sha256:01d0d304b25634d60bd7cf291828119ab55a3bab87dc4af1e44b07fb225f188b \
-    --hash=sha256:0bd308103340030feef6c111d3eb98d50dc13feea33affc8a6f9fa549e9458a3 \
-    --hash=sha256:136ab87b17e733e22f0961504d05e77e7be8c9b5a8184f685b4a91a84efe3c26 \
-    --hash=sha256:16a9c722ba774cf50b5d4541242b4cce05aafd44a015290c82ba8a16931ff63d \
-    --hash=sha256:16c5d0fc45d3aa0a69074979f4f1928ca2734fb2e0dde8af9611e134e46774e7 \
-    --hash=sha256:19efa3611eef966e776183e338b2d7ea43569ae99ab34f8d17c2c054d3205cc0 \
-    --hash=sha256:1d0bfb4b8f661b3b4ec3565fa36c340bfb1cda82087199711f86a88647d26b2f \
-    --hash=sha256:1e7d4d7ad262a48bb44277ca12c7c78cb1b0f56b32c10734ec9a1d30c0b0c54b \
-    --hash=sha256:1f2f33f486777456586948e333a56ae21f35ae273be99255a191f5c1fa302eb4 \
-    --hash=sha256:1ff629e00818033b8d71139565527ced7d776c269a49bd78c9df84e8f852190c \
-    --hash=sha256:21952dc00df38a2c28375659b07a3979d22641aeb104751e769c3ee825aadecf \
-    --hash=sha256:2d93d23bdd2ed9dc157b46bc2f19b7da143ca8714464249bef6771c679d5ff40 \
-    --hash=sha256:2ed4ecb28320a433db18a5bf029986aa8afcfd740745e78847e330d5d94922a9 \
-    --hash=sha256:2fa78b49485391224755a856ed3b3bd91c8455f6121fee0db0e71cefb07d0ef6 \
-    --hash=sha256:346ec0c1a0fcd32f57f00e4134d1200e14bf3f5ae12af87ba83ca275c502498c \
-    --hash=sha256:361b6d45030b4ac64ddd0a26046906c8202eb60d0f9f53085f5179f1d23021a0 \
-    --hash=sha256:40d784101f5d06a1fd30b55fc11ea58a61be23f930d934d86f19a180909908a4 \
-    --hash=sha256:4a44bc62a10d84c11a7a3d7c1c4fe857f7477c3506e24c9062da0db0ae0c449c \
-    --hash=sha256:5860eb9f00a1d49ebb14e881f5caf6c2cf472c7fd38bd7f253bbd34f934eb076 \
-    --hash=sha256:5ebe3f84c6112ad3d4632b1fd2290665aa75d4cef5f6c5d77c4c95b324527c6a \
-    --hash=sha256:61edcd8d0d17dd99075d320a1dd39c0cb9616f7572f10ef91b4b5b00c4aeb566 \
-    --hash=sha256:6305993a35989391bd3476ee538a5c9a845861462327efe00dd11a5c8c709a99 \
-    --hash=sha256:6ddc759f72b5b2b0f54a7e7cde44acef680a55019eb52ac63a7af2cf17cb9cd2 \
-    --hash=sha256:743044b4cfb34f9a67205cee9279feaf60ba7d02e69febc2afc609047cb49179 \
-    --hash=sha256:7ae48c236c0324b4e139bea88a306a04ca630f49be66741b340729d380d8f52f \
-    --hash=sha256:7ed1a5bbfb386ee8332713bf7508bc24e32d24b74fc9a7b9f8529a55db9f4ee6 \
-    --hash=sha256:8cff473e81017594d20ec55d86b54bc635544897e13a7cfc12e36909c5309a2a \
-    --hash=sha256:8d8382dd120d64b03698b7298b89611a6ea6f55ada9d39942838b79c9bc89801 \
-    --hash=sha256:9111274b88e4d7b54a95218e243282709b330ef52b7b86bc6aaf4f805306f454 \
-    --hash=sha256:91121757b0a36c9aac3427a651a7e6567110a4a67c97edf04f8d55d4765f6618 \
-    --hash=sha256:980c777c612514c0cf99bc8a9de6d286f5e186dcaf9091252fcd444e5638193d \
-    --hash=sha256:9a522f460d28e2bf4e12396240a5fc1518788b2fcd73535166d748399ef0c223 \
-    --hash=sha256:9c3a3c648aedc9f99c09263b39f2d8252f199cb3ac154fadc173283d7d111350 \
-    --hash=sha256:a1fd0ea855b2cf7c9cddaf25543e914dd75af9de08785f20ca3085f2c9ca60b0 \
-    --hash=sha256:a444e7afccdb0999e203b976adb37ea633725333e5b119ad40b1ca291ecf311c \
-    --hash=sha256:a84e0582858d841d54355246ddfcbd1fce3179f185da7470f41ce39d001ee1af \
-    --hash=sha256:b510dda1a3672d6879beb319bc7c5fd302c6c354584690973c838f46ec3e0fa8 \
-    --hash=sha256:b54df60f1fbdecc8ebc2c5b11870461a6417b3d617f555e5033f1505d36e5735 \
-    --hash=sha256:b705c99c76695702656327b819c9660768ec33f5ce01fa32b2af62b56ba400a1 \
-    --hash=sha256:ba4d1333cc0bc94381d6d4308d2e4e008e0bd128bdcff5573199742ee3634359 \
-    --hash=sha256:c64d5c97b2f47e3cd9b712eaced3b061f2b71234b3fc263e0fcf7d889c6559dc \
-    --hash=sha256:c8876673449f3401f278c86eb33224c5764582f72b653a415d0e6672fde887bf \
-    --hash=sha256:ca2591bff6645c216695bdf1614fca9c82ea1144d4a7591a466fef64f28f0715 \
-    --hash=sha256:cc4f7397459b12c0b196c9efe1f9d7e92463cbba142632b4cc6d8bbbbd3e2b09 \
-    --hash=sha256:cf831a13e0d5a7eb519e96f58ec26e049b1fad411fc6fc23b162a7ce04d9cffc \
-    --hash=sha256:dc3487447a43d42adcdf52d7ac73804f2312cbfa5d433a7d2c506dcab0033dfd \
-    --hash=sha256:df90e2db118c3671a0754f38e36802db75fe0920d211a27481daf50a711fdf26 \
-    --hash=sha256:e38a190442aacc67ff9f75ce60aec04893041f16f97d242209106d502486a142 \
-    --hash=sha256:e9eddfe513096a71896441a7c37db72da0687b34752c4e193577a145c71736fc \
-    --hash=sha256:eba6710407f1daa8e7602c347dfc94adc02205ec27ed956346190d66579eb9ea \
-    --hash=sha256:ef4a456cc8b7b3d50ccec29642aa4aeda959e9da2fe9540a92754770d5f0cf1f \
-    --hash=sha256:f67b34271dedc3c653eba4e3d7111aa421d5be9b4c4c7d38d30907f796cb30df \
-    --hash=sha256:f6f6cd5819c50d9bcf921882784586aab34a4bd53e7553e175dece6db513a6f0 \
-    --hash=sha256:fe2533caae6a91a543dec62e8360fe86ffcdc42a7c55f9dfd0128a977a896b94 \
-    --hash=sha256:fe7b77dc63d707c09726b7908f575fc04ff1d1ad0f3fb92aec212396bc6cfe5e \
-    --hash=sha256:fe9627c39c59e553c90f5bc3128252cb85dc3b3be8189710666d2f8bc3a5503e
-    # via trimesh
+shapely==2.0.7 \
+    --hash=sha256:0145387565fcf8f7c028b073c802956431308da933ef41d08b1693de49990d27 \
+    --hash=sha256:04a65d882456e13c8b417562c36324c0cd1e5915f3c18ad516bb32ee3f5fc895 \
+    --hash=sha256:06ff6020949b44baa8fc2e5e57e0f3d09486cd5c33b47d669f847c54136e7027 \
+    --hash=sha256:19cbc8808efe87a71150e785b71d8a0e614751464e21fb679d97e274eca7bd43 \
+    --hash=sha256:1a2e03277128e62f9a49a58eb7eb813fa9b343925fca5e7d631d50f4c0e8e0b8 \
+    --hash=sha256:1e9fed9a7d6451979d914cb6ebbb218b4b4e77c0d50da23e23d8327948662611 \
+    --hash=sha256:25085a30a2462cee4e850a6e3fb37431cbbe4ad51cbcc163af0cea1eaa9eb96d \
+    --hash=sha256:28fe2997aab9a9dc026dc6a355d04e85841546b2a5d232ed953e3321ab958ee5 \
+    --hash=sha256:2934834c7f417aeb7cba3b0d9b4441a76ebcecf9ea6e80b455c33c7c62d96a24 \
+    --hash=sha256:2e4a1749ad64bc6e7668c8f2f9479029f079991f4ae3cb9e6b25440e35a4b532 \
+    --hash=sha256:2f6e4759cf680a0f00a54234902415f2fa5fe02f6b05546c662654001f0793a2 \
+    --hash=sha256:33fb10e50b16113714ae40adccf7670379e9ccf5b7a41d0002046ba2b8f0f691 \
+    --hash=sha256:35524cc8d40ee4752520819f9894b9f28ba339a42d4922e92c99b148bed3be39 \
+    --hash=sha256:3697bd078b4459f5a1781015854ef5ea5d824dbf95282d0b60bfad6ff83ec8dc \
+    --hash=sha256:4abeb44b3b946236e4e1a1b3d2a0987fb4d8a63bfb3fdefb8a19d142b72001e5 \
+    --hash=sha256:4c2b9859424facbafa54f4a19b625a752ff958ab49e01bc695f254f7db1835fa \
+    --hash=sha256:5aed1c6764f51011d69a679fdf6b57e691371ae49ebe28c3edb5486537ffbd51 \
+    --hash=sha256:5cf23400cb25deccf48c56a7cdda8197ae66c0e9097fcdd122ac2007e320bc34 \
+    --hash=sha256:5d6dbf096f961ca6bec5640e22e65ccdec11e676344e8157fe7d636e7904fd36 \
+    --hash=sha256:6bca5095e86be9d4ef3cb52d56bdd66df63ff111d580855cb8546f06c3c907cd \
+    --hash=sha256:73c9ae8cf443187d784d57202199bf9fd2d4bb7d5521fe8926ba40db1bc33e8e \
+    --hash=sha256:7977d8a39c4cf0e06247cd2dca695ad4e020b81981d4c82152c996346cf1094b \
+    --hash=sha256:7e97104d28e60b69f9b6a957c4d3a2a893b27525bc1fc96b47b3ccef46726bf2 \
+    --hash=sha256:8ae5cb6b645ac3fba34ad84b32fbdccb2ab321facb461954925bde807a0d3b74 \
+    --hash=sha256:8f623b64bb219d62014781120f47499a7adc30cf7787e24b659e56651ceebcb0 \
+    --hash=sha256:98697c842d5c221408ba8aa573d4f49caef4831e9bc6b6e785ce38aca42d1999 \
+    --hash=sha256:a0c09e3e02f948631c7763b4fd3dd175bc45303a0ae04b000856dedebefe13cb \
+    --hash=sha256:a3fb7fbae257e1b042f440289ee7235d03f433ea880e73e687f108d044b24db5 \
+    --hash=sha256:a7f04691ce1c7ed974c2f8b34a1fe4c3c5dfe33128eae886aa32d730f1ec1913 \
+    --hash=sha256:a9469f49ff873ef566864cb3516091881f217b5d231c8164f7883990eec88b73 \
+    --hash=sha256:aaaf5f7e6cc234c1793f2a2760da464b604584fb58c6b6d7d94144fd2692d67e \
+    --hash=sha256:adeddfb1e22c20548e840403e5e0b3d9dc3daf66f05fa59f1fcf5b5f664f0e98 \
+    --hash=sha256:b52f3ab845d32dfd20afba86675c91919a622f4627182daec64974db9b0b4608 \
+    --hash=sha256:cd0e75d9124b73e06a42bf1615ad3d7d805f66871aa94538c3a9b7871d620013 \
+    --hash=sha256:cf6c50cd879831955ac47af9c907ce0310245f9d162e298703f82e1785e38c98 \
+    --hash=sha256:d8f1da01c04527f7da59ee3755d8ee112cd8967c15fab9e43bba936b81e2a013 \
+    --hash=sha256:dd37d65519b3f8ed8976fa4302a2827cbb96e0a461a2e504db583b08a22f0b98 \
+    --hash=sha256:e1c4f1071fe9c09af077a69b6c75f17feb473caeea0c3579b3e94834efcbdc36 \
+    --hash=sha256:e6d95703efaa64aaabf278ced641b888fc23d9c6dd71f8215091afd8a26a66e3 \
+    --hash=sha256:f44eda8bd7a4bccb0f281264b34bf3518d8c4c9a8ffe69a1a05dabf6e8461147 \
+    --hash=sha256:f86e2c0259fe598c4532acfcf638c1f520fa77c1275912bbc958faecbf00b108 \
+    --hash=sha256:fc19b78cc966db195024d8011649b4e22812f805dd49264323980715ab80accc
+    # via
+    #   nuscenes-devkit
+    #   trimesh
 six==1.17.0 \
     --hash=sha256:4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274 \
     --hash=sha256:ff70335d468e7eb6ec65b95b99d3a2836546063f63acc5171de367e834932a81
@@ -2115,7 +2464,13 @@ tensorflow==2.20.0 \
 termcolor==2.5.0 \
     --hash=sha256:37b17b5fc1e604945c2642c872a3764b5d547a48009871aea3edd3afa180afb8 \
     --hash=sha256:998d8d27da6d48442e8e1f016119076b690d962507531df4890fcd2db2ef8a6f
-    # via tensorflow
+    # via
+    #   fire
+    #   tensorflow
+threadpoolctl==3.6.0 \
+    --hash=sha256:43a0b8fd5a2928500110039e43a5eed8480b918967083ea48dc3ab9f13c4a7fb \
+    --hash=sha256:8ab8b4aa3491d812b623328249fab5302a68d2d71745c8a4c719a2fcaba9f44e
+    # via scikit-learn
 tinycss2==1.4.0 \
     --hash=sha256:10c0972f6fc0fbee87c3edb76549357415e94548c1ae10ebccdea16fb404a9b7 \
     --hash=sha256:3a49cf47b7675da0b15d0c6e1df8df4ebd96e9394bb905a5775adb0d884c5289
@@ -2146,6 +2501,7 @@ tqdm==4.67.1 \
     #   -r deps/pip/requirements_pai.in
     #   -r deps/pip/requirements_tools.in
     #   -r deps/pip/requirements_waymo.in
+    #   nuscenes-devkit
     #   viser
 traitlets==5.14.3 \
     --hash=sha256:9ed0579d3502c94b4b3732ac120375cda96f923114522847de4b3bb98b96b6b7 \
diff --git a/deps/pip/requirements_nuscenes.in b/deps/pip/requirements_nuscenes.in
new file mode 100644
index 00000000..4cfbe852
--- /dev/null
+++ b/deps/pip/requirements_nuscenes.in
@@ -0,0 +1,20 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# nuScenes converter dependencies
+nuscenes-devkit
+# pyquaternion is a transitive dependency of nuscenes-devkit that we also use directly
+# for quaternion-to-rotation-matrix conversion and SLERP interpolation.
+pyquaternion
diff --git a/docs/conversions/index.rst b/docs/conversions/index.rst
index 3e622ec5..be50bee1 100644
--- a/docs/conversions/index.rst
+++ b/docs/conversions/index.rst
@@ -5,13 +5,14 @@ Data Conversions
 ================
 
 NCore provides conversion tools for importing 3rd-party dataset formats into
-the NCore V4 component-based format. Supported formats include KITTI, Waymo,
-COLMAP (including ScanNet++), and PAI.
+the NCore V4 component-based format. Supported formats include KITTI, nuScenes,
+Waymo, COLMAP (including ScanNet++), and PAI.
 
 .. toctree::
    :maxdepth: 1
 
    kitti/kitti
+   nuscenes/nuscenes
    waymo/waymo
    colmap/colmap
    pai/pai
diff --git a/docs/conversions/nuscenes/nuscenes.rst b/docs/conversions/nuscenes/nuscenes.rst
new file mode 100644
index 00000000..881bd33d
--- /dev/null
+++ b/docs/conversions/nuscenes/nuscenes.rst
@@ -0,0 +1,145 @@
+.. SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+.. SPDX-License-Identifier: Apache-2.0
+
+nuScenes Dataset
+================
+
+The NCore nuScenes tool converts data from the
+`nuScenes <https://www.nuscenes.org/>`_ autonomous driving dataset into
+NCore V4 format. All dataset versions are supported (v1.0-mini,
+v1.0-trainval, v1.0-test).
+
+.. _nuscenes_data_conventions:
+
+Conventions
+-----------
+
+The nuScenes dataset provides data from 6 cameras, 1 lidar, and 5
+radars. The converter handles all sensor modalities and 3D annotations.
+
+Camera Sensors
+^^^^^^^^^^^^^^
+    1. **Front (camera_front)** -- 1600x900, 70 deg FOV
+    2. **Front Left (camera_front_left)** -- 1600x900, 70 deg FOV
+    3. **Front Right (camera_front_right)** -- 1600x900, 70 deg FOV
+    4. **Back (camera_back)** -- 1600x900, 110 deg FOV
+    5. **Back Left (camera_back_left)** -- 1600x900, 70 deg FOV
+    6. **Back Right (camera_back_right)** -- 1600x900, 70 deg FOV
+
+All cameras use Basler acA1600-60gc sensors (global shutter). Images are
+provided undistorted with zero distortion coefficients. Camera intrinsics
+are stored using :class:`~ncore.data.OpenCVPinholeCameraModelParameters`
+with ``ShutterType.GLOBAL``.
+
+LiDAR Sensor
+^^^^^^^^^^^^^
+    1. **Top LiDAR (lidar_top)** -- Velodyne HDL-32E, 32 layers, ~34k points/frame
+
+Point clouds in nuScenes are motion-compensated to the sensor frame at the
+sweep reference timestamp. The converter decompensates them back to
+per-point-time sensor frames (raw measurements) before storing, since
+NCore V4 expects non-motion-compensated ray-bundle data.
+
+Per-point timestamps are derived from the column structure of the .bin
+file: each file contains 32-point columns (one per beam) in sequential
+firing order. All points within a column receive the same timestamp.
+Frame time bounds are derived from consecutive sweep timestamps (not a
+hardcoded scan frequency).
+
+A structured lidar model (``RowOffsetStructuredSpinningLidarModelParameters``)
+is derived approximately from a decompensated reference frame and stored as
+intrinsics. The model is approximate because nuScenes only provides
+motion-compensated point clouds -- the original raw sensor data and exact
+per-point timestamps are not available. The model is derived by decompensating
+a reference frame using column-index-based timestamps and then extracting the
+firing geometry from the resulting point cloud:
+
+- ``row_elevations_rad``: per-beam elevation angles (median across valid columns)
+- ``column_azimuths_rad``: per-column azimuth angles from a reference beam row
+- ``row_azimuth_offsets_rad``: per-beam azimuth offsets (intrinsic firing delay)
+- ``spinning_direction``: clockwise ("cw")
+- ``spinning_frequency_hz``: derived from inter-sweep timestamps (~20 Hz)
+
+The ``model_element`` field is populated with ``[ring_index, column_index]``
+per point, enabling structured lidar operations at read time. Column indices
+are assigned via iterative column alignment (matching per-frame decompensated
+azimuths against the static model).
+
+The minimum distance filter (1.0 m) matches the ``remove_close``
+default used by the nuScenes devkit to discard sensor housing
+reflections.
+
+Radar Sensors
+^^^^^^^^^^^^^
+    1. **Front (radar_front)** -- Continental ARS 408
+    2. **Front Left (radar_front_left)** -- Continental ARS 408
+    3. **Front Right (radar_front_right)** -- Continental ARS 408
+    4. **Back Left (radar_back_left)** -- Continental ARS 408
+    5. **Back Right (radar_back_right)** -- Continental ARS 408
+
+Radar detections are sparse (typically 10-100 per sweep). Each detection
+provides position (x, y, z), ego-motion-compensated velocity, and radar
+cross section (RCS). Per-frame generic data fields:
+
+- ``radial_velocity_m_s`` (float32, [N]) -- radial velocity in m/s
+  (positive = moving away from sensor), computed by projecting the
+  ego-motion-compensated velocity vector onto the detection direction.
+- ``rcs_dBsm`` (float32, [N]) -- radar cross section in dBsm.
+
+Radar is not a spinning sensor; all detections in a frame share a single
+timestamp.
+
+Ego Poses
+^^^^^^^^^
+Ego poses are derived from the per-sweep ``ego_pose`` records in the
+nuScenes database (GPS/INS-based). Poses are stored as dynamic
+``("rig", "world")`` poses relative to the first frame. The absolute
+first-frame pose is preserved as a static ``("world", "world_global")``
+transform.
+
+3D Annotations
+^^^^^^^^^^^^^^
+Cuboid annotations are stored in the ``world_global`` coordinate frame
+(the nuScenes global map frame) as
+:class:`~ncore.data.v4.CuboidsComponent` observations. Only keyframe
+annotations are included. The :meth:`~ncore.data.CuboidTrackObservation.transform`
+method can re-project them to any sensor frame at runtime via the pose
+graph.
+
+Category mapping from nuScenes to NCore class IDs:
+
+- vehicle.car -> car
+- vehicle.truck -> truck
+- vehicle.bus.* -> bus
+- vehicle.construction -> construction_vehicle
+- vehicle.motorcycle -> motorcycle
+- vehicle.bicycle -> bicycle
+- vehicle.trailer -> trailer
+- vehicle.emergency.* -> emergency_vehicle
+- human.pedestrian.* -> pedestrian
+- movable_object.barrier -> barrier
+- movable_object.trafficcone -> traffic_cone
+
+Usage
+-----
+
+.. code-block:: bash
+
+   bazel run //tools/data_converter/nuscenes -- \
+       --root-dir /path/to/nuscenes \
+       --output-dir /path/to/output \
+       nuscenes \
+       --version v1.0-trainval
+
+Convert a single scene by name:
+
+.. code-block:: bash
+
+   bazel run //tools/data_converter/nuscenes -- \
+       --root-dir /path/to/nuscenes \
+       --output-dir /path/to/output \
+       nuscenes \
+       --version v1.0-mini \
+       --scene-name scene-0061
+
+See ``tools/data_converter/nuscenes/README.md`` for full option documentation.
diff --git a/tools/data_converter/nuscenes/BUILD.bazel b/tools/data_converter/nuscenes/BUILD.bazel
new file mode 100644
index 00000000..cfc23604
--- /dev/null
+++ b/tools/data_converter/nuscenes/BUILD.bazel
@@ -0,0 +1,92 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+load("@ncore_pip_deps//:requirements.bzl", "requirement")
+load("@rules_python//python:defs.bzl", "py_binary", "py_library")
+load("//bazel/pytest:defs.bzl", "pytest_test")
+
+# nuScenes-specific utilities: scene enumeration, box retrieval
+py_library(
+    name = "pylib_utils",
+    srcs = ["utils.py"],
+    deps = [
+        requirement("numpy"),
+        requirement("nuscenes-devkit"),
+        requirement("pyquaternion"),
+        "//ncore:pylib",
+    ],
+)
+
+# Structured lidar model: derivation, alignment, optimization
+py_library(
+    name = "pylib_lidar_model",
+    srcs = ["lidar_model.py"],
+    deps = [
+        requirement("numpy"),
+        "//ncore:pylib",
+    ],
+)
+
+# Converter library (config, converter class, CLI registration)
+py_library(
+    name = "pylib",
+    srcs = [
+        "converter.py",
+    ],
+    deps = [
+        ":pylib_lidar_model",
+        ":pylib_utils",
+        requirement("click"),
+        requirement("numpy"),
+        requirement("nuscenes-devkit"),
+        requirement("pyquaternion"),
+        requirement("tqdm"),
+        requirement("universal_pathlib"),
+        "//ncore:pylib",
+        "//tools/data_converter:pylib_cli",
+    ],
+)
+
+# Standalone CLI binary
+py_binary(
+    name = "convert",
+    srcs = ["main.py"],
+    main = "main.py",
+    deps = [":pylib"],
+)
+
+alias(
+    name = "nuscenes",
+    actual = ":convert",
+)
+
+# Integration test for the nuScenes converter (requires NUSCENES_DIR env var)
+pytest_test(
+    name = "pytest_converter",
+    srcs = ["converter_test.py"],
+    args = ["--import-mode=importlib"],
+    python_versions = ["3.11"],
+    tags = ["manual"],  # Only run when explicitly requested (needs external data)
+    deps = [
+        ":pylib",
+        requirement("nuscenes-devkit"),
+        requirement("numpy"),
+        requirement("parameterized"),
+        requirement("pyquaternion"),
+        requirement("torch"),
+        requirement("universal_pathlib"),
+        "//ncore:pylib",
+    ],
+)
diff --git a/tools/data_converter/nuscenes/NOTICE b/tools/data_converter/nuscenes/NOTICE
new file mode 100644
index 00000000..bd27cc4a
--- /dev/null
+++ b/tools/data_converter/nuscenes/NOTICE
@@ -0,0 +1,11 @@
+nuScenes Dataset
+Copyright (c) 2019 nuScenes (https://www.nuscenes.org)
+
+This converter processes data from the nuScenes dataset.
+The nuScenes dataset is released under the Creative Commons
+Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0).
+
+Users must agree to the nuScenes Terms of Use before downloading or using the dataset:
+https://www.nuscenes.org/terms-of-use
+
+The nuscenes-devkit library is released under the Apache License 2.0.
diff --git a/tools/data_converter/nuscenes/README.md b/tools/data_converter/nuscenes/README.md
new file mode 100644
index 00000000..c26fefaa
--- /dev/null
+++ b/tools/data_converter/nuscenes/README.md
@@ -0,0 +1,73 @@
+# nuScenes to NCore V4 Converter
+
+Converts [nuScenes](https://www.nuscenes.org/) dataset scenes to NCore V4 format.
+
+## Requirements
+
+- nuScenes dataset downloaded locally (any version: v1.0-mini, v1.0-trainval, v1.0-test)
+- Python packages: `nuscenes-devkit`, `pyquaternion`
+
+## Usage
+
+```bash
+bazel run //tools/data_converter/nuscenes -- \
+    --root-dir /path/to/nuscenes \
+    --output-dir /path/to/output \
+    nuscenes \
+    --version v1.0-trainval
+```
+
+### Convert a single scene by token
+
+```bash
+bazel run //tools/data_converter/nuscenes -- \
+    --root-dir /path/to/nuscenes \
+    --output-dir /path/to/output \
+    nuscenes \
+    --version v1.0-mini \
+    --scene-token cc8c0bf57f984915a77078b10eb33198
+```
+
+### Convert a single scene by name
+
+```bash
+bazel run //tools/data_converter/nuscenes -- \
+    --root-dir /path/to/nuscenes \
+    --output-dir /path/to/output \
+    nuscenes \
+    --version v1.0-mini \
+    --scene-name scene-0061
+```
+
+## Options
+
+| Option | Default | Description |
+|--------|---------|-------------|
+| `--version` | v1.0-trainval | nuScenes version string |
+| `--scene-token` | None | Filter to a single scene by token |
+| `--scene-name` | None | Filter to a single scene by name |
+| `--store-type` | itar | Output store format (itar or directory) |
+| `--profile` | separate-sensors | Component group assignment profile |
+| `--sequence-meta/--no-sequence-meta` | enabled | Generate sequence meta JSON |
+
+## Sensor Assumptions
+
+- **Cameras**: Treated as global shutter (ShutterType.GLOBAL). nuScenes provides a single
+  capture timestamp per image with no rolling-shutter metadata. Images are already undistorted,
+  so all distortion coefficients are zero.
+- **Lidar**: Velodyne HDL-32E spinning lidar at 20 Hz. Source point clouds are
+  motion-compensated; the converter decompensates them to raw per-point-time
+  measurements. Per-point timestamps are derived from the 32-beam column structure
+  in the .bin file. An approximate structured lidar model (elevation/azimuth per
+  beam/column) is derived from a decompensated reference frame and stored as
+  intrinsics. The model is approximate because nuScenes only provides
+  motion-compensated data -- original raw timestamps are not available.
+- **Cuboid annotations**: Stored in the world coordinate frame. Only keyframe annotations
+  are included.
+
+## Testing
+
+```bash
+NUSCENES_DIR=/path/to/nuscenes NUSCENES_VERSION=v1.0-mini \
+    bazel test //tools/data_converter/nuscenes:pytest_converter
+```
diff --git a/tools/data_converter/nuscenes/converter.py b/tools/data_converter/nuscenes/converter.py
new file mode 100644
index 00000000..b4db4d51
--- /dev/null
+++ b/tools/data_converter/nuscenes/converter.py
@@ -0,0 +1,936 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""nuScenes dataset to NCore V4 converter."""
+
+from __future__ import annotations
+
+import json
+import logging
+
+from dataclasses import dataclass, replace
+from typing import Dict, List, Literal, Optional
+
+import click
+import numpy as np
+import tqdm
+
+from nuscenes.utils.data_classes import RadarPointCloud
+from pyquaternion import Quaternion
+from upath import UPath
+
+from ncore.impl.common.transformations import HalfClosedInterval, MotionCompensator, se3_inverse
+from ncore.impl.data.types import (
+    BBox3,
+    CuboidTrackObservation,
+    LabelSource,
+    OpenCVPinholeCameraModelParameters,
+    RowOffsetStructuredSpinningLidarModelParameters,
+    ShutterType,
+)
+from ncore.impl.data.v4.components import (
+    CameraSensorComponent,
+    CuboidsComponent,
+    IntrinsicsComponent,
+    LidarSensorComponent,
+    MasksComponent,
+    PosesComponent,
+    RadarSensorComponent,
+    SequenceComponentGroupsReader,
+    SequenceComponentGroupsWriter,
+)
+from ncore.impl.data.v4.types import ComponentGroupAssignments
+from ncore.impl.data_converter.base import FileBasedDataConverter, FileBasedDataConverterConfig
+from tools.data_converter.cli import cli
+from tools.data_converter.nuscenes.lidar_model import (
+    HDL32E_N_BEAMS,
+    HDL32E_N_TARGET_COLS,
+    HDL32E_SCAN_DURATION_US,
+    LidarFrameData,
+    align_frame,
+    compute_frame_timestamps,
+    compute_model_consistency,
+    decompensate_frame,
+    derive_model_from_decompensated,
+    optimize_model,
+)
+from tools.data_converter.nuscenes.utils import (
+    CAMERA_MAP,
+    LIDAR_CHANNEL,
+    LIDAR_ID,
+    NUSCENES_CATEGORY_MAP,
+    RADAR_MAP,
+    get_boxes_for_sample_data,
+    get_nuscenes,
+    get_sweep_tokens,
+    resolve_scene_token,
+)
+
+
+# -----------------------------------------------------------------------------
+# Config
+# -----------------------------------------------------------------------------
+
+
+@dataclass(kw_only=True, slots=True)
+class NuScenesConverter4Config(FileBasedDataConverterConfig):
+    """Configuration for nuScenes to NCore V4 conversion."""
+
+    version: str = "v1.0-trainval"
+    scene_token: Optional[str] = None
+    scene_name: Optional[str] = None
+    store_type: Literal["itar", "directory"] = "itar"
+    component_group_profile: Literal["default", "separate-sensors", "separate-all"] = "separate-sensors"
+    store_sequence_meta: bool = True
+    lidar_model_optimization_passes: int = 1
+
+
+# -----------------------------------------------------------------------------
+# Converter
+# -----------------------------------------------------------------------------
+
+
+class NuScenesConverter4(FileBasedDataConverter):
+    """Dataset preprocessing class for converting nuScenes data to NCore V4 format.
+
+    Supports nuScenes versions: v1.0-mini, v1.0-trainval, v1.0-test.
+
+    Sensor assumptions:
+    - Cameras: Treated as global shutter (ShutterType.GLOBAL). nuScenes provides a
+      single capture timestamp per image with no rolling-shutter metadata. Images are
+      already undistorted.
+    - Lidar: Velodyne HDL-32E spinning lidar (20 Hz, ~50ms scan duration). Per-point
+      timestamps are derived from the 32-beam column structure in the .bin file.
+      Source data is motion-compensated; we decompensate to raw measurements before
+      storing. Structured lidar model parameters (elevation/azimuth per beam/column)
+      are derived from the first frame's geometry.
+    - Cuboid annotations: Stored in the world coordinate frame. For non-keyframe
+      sweeps, box positions are linearly interpolated between bracketing keyframes.
+    """
+
+    def __init__(self, config: NuScenesConverter4Config) -> None:
+        super().__init__(config)
+
+        self.component_group_profile = config.component_group_profile
+        self.store_type = config.store_type
+        self.store_sequence_meta = config.store_sequence_meta
+        self._lidar_model_optimization_passes = config.lidar_model_optimization_passes
+
+        self._version = config.version
+        self._scene_token = config.scene_token
+        self._scene_name = config.scene_name
+
+        self.logger = logging.getLogger(__name__)
+
+    @staticmethod
+    def get_sequence_ids(config: NuScenesConverter4Config) -> List[str]:
+        """Discover scene tokens to convert."""
+        nusc = get_nuscenes(version=config.version, dataroot=config.root_dir)
+
+        resolved = resolve_scene_token(nusc, config.scene_token, config.scene_name)
+        if resolved is not None:
+            return [resolved]
+
+        # All scenes
+        return [s["token"] for s in nusc.scene]
+
+    @staticmethod
+    def from_config(config: NuScenesConverter4Config) -> NuScenesConverter4:
+        return NuScenesConverter4(config)
+
+    def convert_sequence(self, sequence_id: str) -> None:
+        """Convert a single nuScenes scene to NCore V4 format."""
+        scene_token = sequence_id
+        nusc = get_nuscenes(version=self._version, dataroot=str(self.root_dir))
+        scene_record = nusc.get("scene", scene_token)
+        scene_name = scene_record["name"]
+
+        self.logger.info(f"Converting scene {scene_name} ({scene_token})")
+
+        # Use scene name as output directory (more readable than token)
+        sequence_output_name = scene_name
+
+        # --- Gather lidar sweep timeline (used as pose timeline) ---------------
+        lidar_sweep_tokens = get_sweep_tokens(nusc, scene_record, LIDAR_CHANNEL)
+        lidar_sweep_data = [nusc.get("sample_data", t) for t in lidar_sweep_tokens]
+        lidar_timestamps_us = np.array([sd["timestamp"] for sd in lidar_sweep_data], dtype=np.uint64)
+
+        n_lidar_frames = len(lidar_sweep_tokens)
+        assert n_lidar_frames >= 2, f"Scene has fewer than 2 lidar sweeps: {n_lidar_frames}"
+
+        # --- Decode ego poses from lidar sweep ego_pose records ----------------
+        T_rig_world_list: List[np.ndarray] = []
+        for sd in lidar_sweep_data:
+            ego_pose = nusc.get("ego_pose", sd["ego_pose_token"])
+            T = np.eye(4, dtype=np.float64)
+            T[:3, :3] = Quaternion(ego_pose["rotation"]).rotation_matrix
+            T[:3, 3] = ego_pose["translation"]
+            T_rig_world_list.append(T)
+
+        T_rig_world_all = np.stack(T_rig_world_list)  # [N, 4, 4] float64 (global coords)
+        pose_timestamps_us = lidar_timestamps_us.copy()
+
+        # Store first pose as the world_global anchor (high precision for global coordinates),
+        # then make all poses relative to it (local coords -> float32 sufficient).
+        T_world_world_global = T_rig_world_all[0].copy()  # float64 for global accuracy
+        T_world_global_inv = se3_inverse(T_world_world_global)
+        T_rig_world_relative = (T_world_global_inv @ T_rig_world_all).astype(np.float32)
+
+        # --- Determine active sensors ------------------------------------------
+        camera_ids = self.get_active_camera_ids(list(CAMERA_MAP.keys()))
+        lidar_ids = self.get_active_lidar_ids([LIDAR_ID])
+        radar_ids = self.get_active_radar_ids(list(RADAR_MAP.keys()))
+
+        # --- Compute sequence time interval ------------------------------------
+        # Per-point timestamps span [prev_sweep_ts, current_sweep_ts] for each frame.
+        # For the first frame, extrapolate backward using the gap to the next frame.
+        if len(lidar_timestamps_us) >= 2:
+            first_gap = int(lidar_timestamps_us[1]) - int(lidar_timestamps_us[0])
+        else:
+            first_gap = HDL32E_SCAN_DURATION_US
+        seq_start_us = int(lidar_timestamps_us[0]) - first_gap
+        seq_end_us = int(lidar_timestamps_us[-1])
+
+        # Also include camera and radar timestamps for full coverage
+        for ncore_cam_id, nusc_channel in CAMERA_MAP.items():
+            if ncore_cam_id not in camera_ids:
+                continue
+            cam_tokens = get_sweep_tokens(nusc, scene_record, nusc_channel)
+            if cam_tokens:
+                cam_data = [nusc.get("sample_data", t) for t in cam_tokens]
+                cam_ts = [sd["timestamp"] for sd in cam_data]
+                seq_start_us = min(seq_start_us, min(cam_ts))
+                seq_end_us = max(seq_end_us, max(cam_ts))
+
+        for ncore_radar_id, nusc_channel in RADAR_MAP.items():
+            if ncore_radar_id not in radar_ids:
+                continue
+            radar_tokens = get_sweep_tokens(nusc, scene_record, nusc_channel)
+            if radar_tokens:
+                radar_data = [nusc.get("sample_data", t) for t in radar_tokens]
+                radar_ts = [sd["timestamp"] for sd in radar_data]
+                seq_start_us = min(seq_start_us, min(radar_ts))
+                seq_end_us = max(seq_end_us, max(radar_ts))
+
+        sequence_timestamp_interval_us = HalfClosedInterval.from_start_end(seq_start_us, seq_end_us)
+
+        # Extend pose timestamps to cover sequence interval boundaries.
+        # For the start boundary, extrapolate backward using the motion between
+        # the first two poses (constant-velocity assumption). This is critical
+        # for the first lidar frame's decompensation -- if we just replicate the
+        # first pose, the decompensator sees zero motion and produces no correction.
+        if seq_start_us < int(pose_timestamps_us[0]):
+            if len(T_rig_world_relative) >= 2:
+                # Extrapolate: apply the inverse of the motion from 0->1 to get the pose before 0.
+                T_0 = T_rig_world_relative[0]
+                T_1 = T_rig_world_relative[1]
+                T_delta_inv = se3_inverse(T_1) @ T_0
+                T_boundary = (T_0 @ T_delta_inv).astype(np.float32)
+                T_rig_world_relative = np.concatenate([T_boundary[np.newaxis], T_rig_world_relative], axis=0)
+            else:
+                T_rig_world_relative = np.concatenate([T_rig_world_relative[:1], T_rig_world_relative], axis=0)
+            pose_timestamps_us = np.concatenate([np.array([seq_start_us], dtype=np.uint64), pose_timestamps_us])
+
+        if seq_end_us > int(pose_timestamps_us[-1]):
+            if len(T_rig_world_relative) >= 2:
+                # Extrapolate forward using constant-velocity from last two poses.
+                T_n1 = T_rig_world_relative[-2]
+                T_n = T_rig_world_relative[-1]
+                T_delta = se3_inverse(T_n1) @ T_n
+                T_boundary = (T_n @ T_delta).astype(np.float32)
+                T_rig_world_relative = np.concatenate([T_rig_world_relative, T_boundary[np.newaxis]], axis=0)
+            else:
+                T_rig_world_relative = np.concatenate([T_rig_world_relative, T_rig_world_relative[-1:]], axis=0)
+            pose_timestamps_us = np.concatenate([pose_timestamps_us, np.array([seq_end_us], dtype=np.uint64)])
+
+        # --- Component group assignments --------------------------------------
+        component_groups = ComponentGroupAssignments.create(
+            camera_ids=camera_ids,
+            lidar_ids=lidar_ids,
+            radar_ids=radar_ids,
+            point_clouds_ids=[],
+            camera_labels_ids=[],
+            profile=self.component_group_profile,
+        )
+
+        # --- Create writer ----------------------------------------------------
+        store_writer = SequenceComponentGroupsWriter(
+            output_dir_path=self.output_dir / sequence_output_name,
+            store_base_name=sequence_output_name,
+            sequence_id=sequence_output_name,
+            sequence_timestamp_interval_us=sequence_timestamp_interval_us,
+            store_type=self.store_type,
+            generic_meta_data={
+                "source_dataset": "nuscenes",
+                "nuscenes_version": self._version,
+                "nuscenes_scene_token": scene_token,
+                "nuscenes_scene_name": scene_name,
+            },
+        )
+
+        # --- Register component writers ---------------------------------------
+        poses_writer = store_writer.register_component_writer(
+            PosesComponent.Writer,
+            component_instance_name="default",
+            group_name=component_groups.poses_component_group,
+            generic_meta_data={
+                "calibration_type": "nuscenes:calibrated_sensor",
+                "egomotion_type": "nuscenes:ego_pose",
+            },
+        )
+
+        intrinsics_writer = store_writer.register_component_writer(
+            IntrinsicsComponent.Writer,
+            component_instance_name="default",
+            group_name=component_groups.intrinsics_component_group,
+        )
+
+        masks_writer = store_writer.register_component_writer(
+            MasksComponent.Writer,
+            component_instance_name="default",
+            group_name=component_groups.masks_component_group,
+        )
+
+        # --- Store ego poses --------------------------------------------------
+        poses_writer.store_dynamic_pose(
+            source_frame_id="rig",
+            target_frame_id="world",
+            poses=T_rig_world_relative,
+            timestamps_us=pose_timestamps_us,
+        )
+
+        poses_writer.store_static_pose(
+            source_frame_id="world",
+            target_frame_id="world_global",
+            pose=T_world_world_global,
+        )
+
+        # --- Decode lidar -----------------------------------------------------
+        if LIDAR_ID in lidar_ids:
+            self._decode_lidar(
+                nusc=nusc,
+                store_writer=store_writer,
+                poses_writer=poses_writer,
+                intrinsics_writer=intrinsics_writer,
+                component_groups=component_groups,
+                lidar_sweep_tokens=lidar_sweep_tokens,
+                lidar_sweep_data=lidar_sweep_data,
+                T_rig_world_relative=T_rig_world_relative,
+                pose_timestamps_us=pose_timestamps_us,
+            )
+
+        # --- Decode cameras ---------------------------------------------------
+        self._decode_cameras(
+            nusc=nusc,
+            scene_record=scene_record,
+            store_writer=store_writer,
+            poses_writer=poses_writer,
+            intrinsics_writer=intrinsics_writer,
+            masks_writer=masks_writer,
+            component_groups=component_groups,
+            camera_ids=camera_ids,
+        )
+
+        # --- Decode radars ----------------------------------------------------
+        self._decode_radars(
+            nusc=nusc,
+            scene_record=scene_record,
+            store_writer=store_writer,
+            poses_writer=poses_writer,
+            component_groups=component_groups,
+            radar_ids=radar_ids,
+        )
+
+        # --- Decode cuboid annotations ----------------------------------------
+        self._decode_cuboids(
+            nusc=nusc,
+            store_writer=store_writer,
+            component_groups=component_groups,
+            lidar_sweep_tokens=lidar_sweep_tokens,
+            lidar_sweep_data=lidar_sweep_data,
+        )
+
+        # --- Finalize ---------------------------------------------------------
+        ncore_4_paths = store_writer.finalize()
+
+        if self.store_sequence_meta:
+            sequence_component_reader = SequenceComponentGroupsReader(ncore_4_paths)
+            sequence_meta_path = (
+                self.output_dir / sequence_output_name / f"{sequence_component_reader.sequence_id}.json"
+            )
+            with sequence_meta_path.open("w") as f:
+                json.dump(sequence_component_reader.get_sequence_meta().to_dict(), f, indent=2)
+            self.logger.info(f"Wrote sequence meta data {str(sequence_meta_path)}")
+
+    # -------------------------------------------------------------------------
+    # Lidar
+    # -------------------------------------------------------------------------
+
+    def _decode_lidar(
+        self,
+        nusc,
+        store_writer: SequenceComponentGroupsWriter,
+        poses_writer: PosesComponent.Writer,
+        intrinsics_writer: IntrinsicsComponent.Writer,
+        component_groups: ComponentGroupAssignments,
+        lidar_sweep_tokens: List[str],
+        lidar_sweep_data: List[Dict],
+        T_rig_world_relative: np.ndarray,
+        pose_timestamps_us: np.ndarray,
+    ) -> None:
+        """Decode and store all lidar frames.
+
+        nuScenes point clouds are motion-compensated to the sensor frame at the sweep's
+        reference timestamp. We decompensate them back to per-point-time sensor frames
+        before storing, since NCore V4 expects raw (non-motion-compensated) measurements.
+
+        Per-point timestamps are derived from the model column index (constant angular
+        velocity: one full rotation per frame). A structured lidar model with per-row
+        azimuth offsets is extracted from a reference frame and used for column alignment.
+        """
+        # Get extrinsic from calibrated_sensor (lidar -> rig)
+        calibrated_sensor = nusc.get("calibrated_sensor", lidar_sweep_data[0]["calibrated_sensor_token"])
+        T_lidar_rig = np.eye(4, dtype=np.float32)
+        T_lidar_rig[:3, :3] = Quaternion(calibrated_sensor["rotation"]).rotation_matrix
+        T_lidar_rig[:3, 3] = calibrated_sensor["translation"]
+
+        # Store static extrinsic pose (lidar -> rig)
+        poses_writer.store_static_pose(source_frame_id=LIDAR_ID, target_frame_id="rig", pose=T_lidar_rig)
+
+        # Initialize motion compensator for decompensation
+        motion_compensator = MotionCompensator.from_sensor_rig(
+            sensor_id=LIDAR_ID,
+            T_sensor_rig=T_lidar_rig,
+            T_rig_worlds=T_rig_world_relative,
+            T_rig_worlds_timestamps_us=pose_timestamps_us,
+        )
+
+        # Register lidar component writer
+        lidar_writer = store_writer.register_component_writer(
+            LidarSensorComponent.Writer,
+            component_instance_name=LIDAR_ID,
+            group_name=component_groups.lidar_component_groups.get(LIDAR_ID),
+            generic_meta_data={"sensor_model": "Velodyne HDL-32E"},
+        )
+
+        # Precompute frame start timestamps from consecutive sweep times.
+        frame_start_timestamps = []
+        for i in range(len(lidar_sweep_data)):
+            if i == 0:
+                if len(lidar_sweep_data) >= 2:
+                    gap = int(lidar_sweep_data[1]["timestamp"]) - int(lidar_sweep_data[0]["timestamp"])
+                else:
+                    gap = HDL32E_SCAN_DURATION_US
+                frame_start_timestamps.append(int(lidar_sweep_data[0]["timestamp"]) - gap)
+            else:
+                frame_start_timestamps.append(int(lidar_sweep_data[i - 1]["timestamp"]))
+
+        # --- Derive lidar model from a "good" frame ---
+        # Find the first frame with the target column count, decompensate it, and
+        # extract the model from the decompensated azimuths.
+        lidar_model_parameters: RowOffsetStructuredSpinningLidarModelParameters | None = None
+
+        for j, sd_j in enumerate(lidar_sweep_data):
+            scan_j = np.fromfile(str(UPath(str(self.root_dir)) / sd_j["filename"]), dtype=np.float32).reshape(-1, 5)
+            xyz_j = scan_j[:, :3].astype(np.float32)
+
+            if len(xyz_j) // HDL32E_N_BEAMS != HDL32E_N_TARGET_COLS:
+                continue
+
+            frame_end_j = int(sd_j["timestamp"])
+            frame_start_j = frame_start_timestamps[j]
+            frame_duration_s = (frame_end_j - frame_start_j) / 1e6
+            freq_hz = 1.0 / frame_duration_s if frame_duration_s > 0 else 20.0
+
+            # Decompensate using column-index timestamps
+
+            n_pts_j = len(xyz_j)
+            col_idx_j = np.arange(n_pts_j) // HDL32E_N_BEAMS
+            ts_j = compute_frame_timestamps(col_idx_j, HDL32E_N_TARGET_COLS, frame_start_j, frame_end_j)
+
+            xyz_decomp_j = decompensate_frame(xyz_j, ts_j, frame_start_j, frame_end_j, motion_compensator, LIDAR_ID)
+
+            lidar_model_parameters = derive_model_from_decompensated(
+                xyz_decompensated=xyz_decomp_j,
+                n_beams_per_column=HDL32E_N_BEAMS,
+                n_target_cols=HDL32E_N_TARGET_COLS,
+                spinning_direction="cw",
+                spinning_frequency_hz=freq_hz,
+            )
+            if lidar_model_parameters is not None:
+                self.logger.info(f"Derived lidar model from frame {j} (n_cols={lidar_model_parameters.n_columns})")
+                break
+
+        assert lidar_model_parameters is not None, (
+            f"Failed to derive lidar model: no frame with {HDL32E_N_TARGET_COLS} columns found"
+        )
+        n_model_cols = lidar_model_parameters.n_columns
+
+        # --- Process each frame ---
+        optimization_data: List[LidarFrameData] = []
+
+        for i, (_, sd) in enumerate(
+            tqdm.tqdm(
+                zip(lidar_sweep_tokens, lidar_sweep_data),
+                total=len(lidar_sweep_tokens),
+                desc=f"Process {LIDAR_ID}",
+            )
+        ):
+            source_pc_path = UPath(str(self.root_dir)) / sd["filename"]
+            scan = np.fromfile(str(source_pc_path), dtype=np.float32).reshape(-1, 5)
+            xyz_mc = scan[:, :3].astype(np.float32)
+            raw_intensity = scan[:, 3]
+            ring_index = scan[:, 4].astype(np.uint16)
+            intensity = (raw_intensity / 255.0).astype(np.float32)
+
+            frame_end_us = int(sd["timestamp"])
+            frame_start_us = frame_start_timestamps[i]
+
+            # Align and decompensate using modular pipeline
+            frame_data = align_frame(
+                xyz_mc=xyz_mc,
+                ring_index=ring_index,
+                intensity=intensity,
+                n_beams_per_column=HDL32E_N_BEAMS,
+                model_params=lidar_model_parameters,
+                motion_compensator=motion_compensator,
+                sensor_id=LIDAR_ID,
+                frame_start_us=frame_start_us,
+                frame_end_us=frame_end_us,
+            )
+
+            if frame_data is None:
+                continue
+
+            # Collect data for optional multi-frame optimization
+            if self._lidar_model_optimization_passes > 0:
+                optimization_data.append(frame_data)
+
+            # Compute direction and distance from decompensated points
+            distance_m = np.linalg.norm(frame_data.xyz_decompensated, axis=1).astype(np.float32)
+            direction = np.zeros_like(frame_data.xyz_decompensated)
+            nonzero_mask = distance_m > 0
+            direction[nonzero_mask] = frame_data.xyz_decompensated[nonzero_mask] / distance_m[nonzero_mask, np.newaxis]
+
+            lidar_writer.store_frame(
+                direction=direction,
+                timestamp_us=frame_data.timestamps_us,
+                model_element=frame_data.model_element,
+                distance_m=distance_m.reshape(1, -1),
+                intensity=frame_data.intensity.reshape(1, -1),
+                frame_timestamps_us=np.array([frame_start_us, frame_end_us], dtype=np.uint64),
+                generic_data={},
+                generic_meta_data={},
+            )
+
+        # --- Optional: multi-frame model optimization ---
+        if self._lidar_model_optimization_passes > 0 and optimization_data:
+            frame_azimuths = []
+            frame_model_cols = []
+            frame_model_rows = []
+            frame_distances = []
+
+            for fd in optimization_data:
+                az = np.arctan2(fd.xyz_decompensated[:, 1], fd.xyz_decompensated[:, 0]).astype(np.float64)
+                frame_azimuths.append(az)
+                frame_model_cols.append(fd.model_element[:, 1].astype(np.int64))
+                frame_model_rows.append(fd.model_element[:, 0].astype(np.int64))
+                frame_distances.append(np.linalg.norm(fd.xyz_decompensated, axis=1))
+
+            lidar_model_parameters = optimize_model(
+                model_params=lidar_model_parameters,
+                frame_azimuths=frame_azimuths,
+                frame_model_cols=frame_model_cols,
+                frame_model_rows=frame_model_rows,
+                frame_distances=frame_distances,
+                min_range_m=10.0,
+                n_iterations=self._lidar_model_optimization_passes,
+            )
+            self.logger.info(
+                f"Optimized lidar model across {len(optimization_data)} frames "
+                f"({self._lidar_model_optimization_passes} iterations)"
+            )
+
+        # Store lidar intrinsics (structured model, possibly optimized)
+        intrinsics_writer.store_lidar_intrinsics(
+            lidar_id=LIDAR_ID,
+            lidar_model_parameters=lidar_model_parameters,
+        )
+
+    # -------------------------------------------------------------------------
+    # Cameras
+    # -------------------------------------------------------------------------
+
+    def _decode_cameras(
+        self,
+        nusc,
+        scene_record: Dict,
+        store_writer: SequenceComponentGroupsWriter,
+        poses_writer: PosesComponent.Writer,
+        intrinsics_writer: IntrinsicsComponent.Writer,
+        masks_writer: MasksComponent.Writer,
+        component_groups: ComponentGroupAssignments,
+        camera_ids: List[str],
+    ) -> None:
+        """Decode and store all camera frames."""
+        for ncore_cam_id, nusc_channel in CAMERA_MAP.items():
+            if ncore_cam_id not in camera_ids:
+                continue
+
+            self.logger.info(f"Processing camera {ncore_cam_id} ({nusc_channel})")
+
+            cam_sweep_tokens = get_sweep_tokens(nusc, scene_record, nusc_channel)
+            cam_sweep_data = [nusc.get("sample_data", t) for t in cam_sweep_tokens]
+
+            if not cam_sweep_data:
+                self.logger.warning(f"No data for camera {nusc_channel}")
+                continue
+
+            # Get calibration from first sweep
+            calibrated_sensor = nusc.get("calibrated_sensor", cam_sweep_data[0]["calibrated_sensor_token"])
+
+            # Camera extrinsic: sensor -> rig
+            T_cam_rig = np.eye(4, dtype=np.float32)
+            T_cam_rig[:3, :3] = Quaternion(calibrated_sensor["rotation"]).rotation_matrix
+            T_cam_rig[:3, 3] = calibrated_sensor["translation"]
+
+            # Store camera extrinsic
+            poses_writer.store_static_pose(
+                source_frame_id=ncore_cam_id,
+                target_frame_id="rig",
+                pose=T_cam_rig,
+            )
+
+            # Parse intrinsics
+            I_cam = np.array(calibrated_sensor["camera_intrinsic"], dtype=np.float32)  # [3, 3]
+            width = int(cam_sweep_data[0]["width"])
+            height = int(cam_sweep_data[0]["height"])
+
+            # Store camera intrinsics
+            # nuScenes images are undistorted, so all distortion coefficients are zero.
+            # ShutterType.GLOBAL: nuScenes provides a single capture timestamp per image
+            # with no rolling-shutter metadata available.
+            intrinsics_writer.store_camera_intrinsics(
+                camera_id=ncore_cam_id,
+                camera_model_parameters=OpenCVPinholeCameraModelParameters(
+                    resolution=np.array([width, height], dtype=np.uint64),
+                    shutter_type=ShutterType.GLOBAL,
+                    external_distortion_parameters=None,
+                    principal_point=np.array([I_cam[0, 2], I_cam[1, 2]], dtype=np.float32),
+                    focal_length=np.array([I_cam[0, 0], I_cam[1, 1]], dtype=np.float32),
+                    radial_coeffs=np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0], dtype=np.float32),
+                    tangential_coeffs=np.array([0.0, 0.0], dtype=np.float32),
+                    thin_prism_coeffs=np.array([0.0, 0.0, 0.0, 0.0], dtype=np.float32),
+                ),
+            )
+
+            # Store empty masks
+            masks_writer.store_camera_masks(
+                camera_id=ncore_cam_id,
+                mask_images={},
+            )
+
+            # Register camera component writer
+            camera_writer = store_writer.register_component_writer(
+                CameraSensorComponent.Writer,
+                component_instance_name=ncore_cam_id,
+                group_name=component_groups.camera_component_groups.get(ncore_cam_id),
+                generic_meta_data={},
+            )
+
+            # Store frames
+            for sd in tqdm.tqdm(cam_sweep_data, desc=f"Process {ncore_cam_id}"):
+                image_path = UPath(str(self.root_dir)) / sd["filename"]
+
+                with image_path.open("rb") as f:
+                    image_binary = f.read()
+
+                # Global shutter: frame start == frame end timestamp
+                frame_ts = int(sd["timestamp"])
+
+                camera_writer.store_frame(
+                    image_binary_data=image_binary,
+                    image_format="jpeg",
+                    frame_timestamps_us=np.array([frame_ts, frame_ts], dtype=np.uint64),
+                    generic_data={},
+                    generic_meta_data={},
+                )
+
+        self.logger.info(f"Processed {len(camera_ids)} cameras")
+
+    # -------------------------------------------------------------------------
+    # Radars
+    # -------------------------------------------------------------------------
+
+    def _decode_radars(
+        self,
+        nusc,
+        scene_record: Dict,
+        store_writer: SequenceComponentGroupsWriter,
+        poses_writer: PosesComponent.Writer,
+        component_groups: ComponentGroupAssignments,
+        radar_ids: List[str],
+    ) -> None:
+        """Decode and store all radar frames.
+
+        nuScenes radars (Continental ARS 408) provide sparse detections with
+        Cartesian position (x, y, z), ego-motion-compensated velocity (vx_comp,
+        vy_comp), and radar cross section (rcs). Data is stored in .pcd files
+        with 18 fields per detection.
+
+        We compute radial velocity by projecting the compensated velocity vector
+        onto the detection direction (positive = moving away from sensor).
+        """
+
+        for ncore_radar_id, nusc_channel in RADAR_MAP.items():
+            if ncore_radar_id not in radar_ids:
+                continue
+
+            self.logger.info(f"Processing radar {ncore_radar_id} ({nusc_channel})")
+
+            radar_sweep_tokens = get_sweep_tokens(nusc, scene_record, nusc_channel)
+            radar_sweep_data = [nusc.get("sample_data", t) for t in radar_sweep_tokens]
+
+            if not radar_sweep_data:
+                self.logger.warning(f"No data for radar {nusc_channel}")
+                continue
+
+            # Get calibration (radar -> rig extrinsic)
+            calibrated_sensor = nusc.get("calibrated_sensor", radar_sweep_data[0]["calibrated_sensor_token"])
+            T_radar_rig = np.eye(4, dtype=np.float32)
+            T_radar_rig[:3, :3] = Quaternion(calibrated_sensor["rotation"]).rotation_matrix
+            T_radar_rig[:3, 3] = calibrated_sensor["translation"]
+
+            # Store radar extrinsic
+            poses_writer.store_static_pose(
+                source_frame_id=ncore_radar_id,
+                target_frame_id="rig",
+                pose=T_radar_rig,
+            )
+
+            # Register radar component writer
+            radar_writer = store_writer.register_component_writer(
+                RadarSensorComponent.Writer,
+                component_instance_name=ncore_radar_id,
+                group_name=component_groups.radar_component_groups.get(ncore_radar_id),
+                generic_meta_data={
+                    "sensor_model": "Continental ARS 408",
+                },
+            )
+
+            # Store frames
+            for sd in tqdm.tqdm(radar_sweep_data, desc=f"Process {ncore_radar_id}"):
+                radar_path = UPath(str(self.root_dir)) / sd["filename"]
+
+                # Load radar point cloud (18 fields)
+                pc = RadarPointCloud.from_file(str(radar_path))
+                pts = pc.points.T  # [N, 18]
+
+                if len(pts) == 0:
+                    continue
+
+                # Extract fields
+                xyz = pts[:, :3].astype(np.float32)  # x, y, z in sensor frame
+                rcs = pts[:, 5].astype(np.float32)  # radar cross section (dBsm)
+                vx_comp = pts[:, 8].astype(np.float32)  # ego-motion-compensated velocity x
+                vy_comp = pts[:, 9].astype(np.float32)  # ego-motion-compensated velocity y
+
+                # Compute distance and direction
+                distance = np.linalg.norm(xyz, axis=1).astype(np.float32)
+                valid_mask = distance > 0.1  # filter degenerate detections
+
+                if not valid_mask.any():
+                    continue
+
+                xyz = xyz[valid_mask]
+                distance = distance[valid_mask]
+                rcs = rcs[valid_mask]
+                vx_comp = vx_comp[valid_mask]
+                vy_comp = vy_comp[valid_mask]
+
+                direction = (xyz / distance[:, np.newaxis]).astype(np.float32)
+
+                # Compute radial velocity: project compensated velocity onto direction
+                # Positive = moving away from sensor
+                velocity_vec = np.stack([vx_comp, vy_comp, np.zeros_like(vx_comp)], axis=1)
+                radial_velocity = np.sum(velocity_vec * direction, axis=1).astype(np.float32)
+
+                # Radar is not a spinning sensor -- all detections share one timestamp
+                frame_ts = int(sd["timestamp"])
+                timestamp_us = np.full(len(xyz), frame_ts, dtype=np.uint64)
+
+                radar_writer.store_frame(
+                    direction=direction,
+                    timestamp_us=timestamp_us,
+                    distance_m=distance.reshape(1, -1),  # [1, N] single return
+                    frame_timestamps_us=np.array([frame_ts, frame_ts], dtype=np.uint64),
+                    generic_data={
+                        "radial_velocity_m_s": radial_velocity,
+                        "rcs_dBsm": rcs,
+                    },
+                    generic_meta_data={},
+                )
+
+        self.logger.info(f"Processed {len(radar_ids)} radars")
+
+    # -------------------------------------------------------------------------
+    # Cuboid annotations
+    # -------------------------------------------------------------------------
+
+    def _decode_cuboids(
+        self,
+        nusc,
+        store_writer: SequenceComponentGroupsWriter,
+        component_groups: ComponentGroupAssignments,
+        lidar_sweep_tokens: List[str],
+        lidar_sweep_data: List[Dict],
+    ) -> None:
+        """Decode nuScenes 3D annotations and store as cuboid track observations.
+
+        Annotations are stored in the world coordinate frame. For non-keyframe sweeps,
+        box positions are interpolated between bracketing keyframes.
+        """
+        cuboid_observations: List[CuboidTrackObservation] = []
+
+        for token, sd in tqdm.tqdm(
+            zip(lidar_sweep_tokens, lidar_sweep_data),
+            total=len(lidar_sweep_tokens),
+            desc="Process cuboids",
+        ):
+            # Only process keyframes (annotations are defined at keyframes)
+            if not sd["is_key_frame"]:
+                continue
+
+            boxes = get_boxes_for_sample_data(nusc, token)
+            timestamp_us = int(sd["timestamp"])
+
+            for box in boxes:
+                # Filter to mapped categories only
+                if box.name not in NUSCENES_CATEGORY_MAP:
+                    continue
+
+                class_id = NUSCENES_CATEGORY_MAP[box.name]
+
+                # nuScenes Box: center=[x,y,z] in global frame, wlh=[width, length, height]
+                # BBox3 format: [cx, cy, cz, size_x, size_y, size_z, rx, ry, rz]
+                # nuScenes wlh order is [width, length, height]
+                # Heading: extract yaw from quaternion
+                yaw = Quaternion(box.orientation).yaw_pitch_roll[0]
+
+                bbox3 = BBox3.from_array(
+                    np.array(
+                        [
+                            box.center[0],
+                            box.center[1],
+                            box.center[2],
+                            box.wlh[1],  # length -> size_x
+                            box.wlh[0],  # width -> size_y
+                            box.wlh[2],  # height -> size_z
+                            0.0,  # rx (pitch) -- only yaw used
+                            0.0,  # ry (roll) -- only yaw used
+                            yaw,  # rz (yaw)
+                        ],
+                        dtype=np.float32,
+                    )
+                )
+
+                cuboid_observations.append(
+                    CuboidTrackObservation(
+                        track_id=box.token,  # instance_token as track ID
+                        class_id=class_id,
+                        timestamp_us=timestamp_us,
+                        reference_frame_id="world_global",
+                        reference_frame_timestamp_us=timestamp_us,
+                        bbox3=bbox3,
+                        source=LabelSource.EXTERNAL,
+                    )
+                )
+
+        if cuboid_observations:
+            store_writer.register_component_writer(
+                CuboidsComponent.Writer,
+                "default",
+                component_groups.cuboid_track_observations_component_group,
+            ).store_observations(cuboid_observations)
+
+            self.logger.info(f"Stored {len(cuboid_observations)} cuboid observations")
+        else:
+            self.logger.info("No cuboid annotations found (test split or empty scenes)")
+
+
+# -----------------------------------------------------------------------------
+# CLI
+# -----------------------------------------------------------------------------
+
+
+@cli.command()
+@click.option(
+    "--version",
+    "nuscenes_version",
+    type=str,
+    default="v1.0-trainval",
+    show_default=True,
+    help="nuScenes dataset version (v1.0-mini, v1.0-trainval, v1.0-test)",
+)
+@click.option(
+    "--scene-token",
+    type=str,
+    default=None,
+    help="Convert only the scene with this token (mutually exclusive with --scene-name)",
+)
+@click.option(
+    "--scene-name",
+    type=str,
+    default=None,
+    help="Convert only the scene with this name, e.g. 'scene-0001' (mutually exclusive with --scene-token)",
+)
+@click.option(
+    "--store-type",
+    type=click.Choice(["itar", "directory"], case_sensitive=False),
+    default="itar",
+    show_default=True,
+    help="Output store type",
+)
+@click.option(
+    "component_group_profile",
+    "--profile",
+    type=click.Choice(["default", "separate-sensors", "separate-all"], case_sensitive=False),
+    default="separate-sensors",
+    show_default=True,
+    help="Output profile for component group assignment",
+)
+@click.option(
+    "store_sequence_meta",
+    "--sequence-meta/--no-sequence-meta",
+    default=True,
+    help="Generate sequence meta-data JSON?",
+)
+@click.option(
+    "lidar_model_optimization_passes",
+    "--lidar-model-optimization-passes",
+    type=int,
+    default=1,
+    show_default=True,
+    help="Number of multi-frame optimization passes for the lidar model (0 to disable).",
+)
+@click.pass_context
+def nuscenes(ctx, nuscenes_version, scene_token, scene_name, **kwargs):
+    """nuScenes data conversion (V4 format)"""
+
+    config = NuScenesConverter4Config(
+        **{**vars(ctx.obj), "version": nuscenes_version, "scene_token": scene_token, "scene_name": scene_name, **kwargs}
+    )
+
+    NuScenesConverter4.convert(config)
diff --git a/tools/data_converter/nuscenes/converter_test.py b/tools/data_converter/nuscenes/converter_test.py
new file mode 100644
index 00000000..7b400602
--- /dev/null
+++ b/tools/data_converter/nuscenes/converter_test.py
@@ -0,0 +1,403 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Integration tests for the nuScenes data converter (V4 format).
+
+Requires the NUSCENES_DIR environment variable pointing to a nuScenes dataset root
+directory (e.g. /data/nuscenes). Works with any version but v1.0-mini is recommended
+for CI since it is small (~4GB).
+
+Set NUSCENES_VERSION to override the default (v1.0-mini for tests).
+"""
+
+import os
+import tempfile
+import unittest
+
+from typing import Literal, cast
+
+import numpy as np
+import torch
+
+from parameterized import parameterized_class
+from upath import UPath
+
+from ncore.impl.data.types import OpenCVPinholeCameraModelParameters, RowOffsetStructuredSpinningLidarModelParameters
+from ncore.impl.data.v4.components import (
+    CameraSensorComponent,
+    CuboidsComponent,
+    IntrinsicsComponent,
+    LidarSensorComponent,
+    PosesComponent,
+    RadarSensorComponent,
+    SequenceComponentGroupsReader,
+)
+from ncore.impl.sensors.lidar import StructuredLidarModel
+from tools.data_converter.nuscenes.converter import NuScenesConverter4, NuScenesConverter4Config
+from tools.data_converter.nuscenes.utils import get_nuscenes
+
+
+@parameterized_class(
+    ("store_type",),
+    [
+        ("itar",),
+        ("directory",),
+    ],
+)
+class TestNuScenesConverter(unittest.TestCase):
+    """Integration tests for nuScenes data converter.
+
+    Requires NUSCENES_DIR environment variable pointing to a nuScenes dataset root.
+    Uses the first scene in the dataset for testing.
+    """
+
+    store_type: Literal["itar", "directory"]
+
+    @classmethod
+    def setUpClass(cls):
+        cls.nuscenes_dir = os.environ.get("NUSCENES_DIR")
+        if cls.nuscenes_dir is None:
+            raise unittest.SkipTest("NUSCENES_DIR not set -- skipping nuScenes integration tests")
+
+        cls.nuscenes_version = os.environ.get("NUSCENES_VERSION", "v1.0-mini")
+
+        cls._tempdir = tempfile.TemporaryDirectory(prefix="nuscenes_test_")
+        cls.output_dir = cls._tempdir.name
+
+        # Run the converter for the first scene only
+
+        nusc = get_nuscenes(version=cls.nuscenes_version, dataroot=cls.nuscenes_dir)
+        cls.scene_token = nusc.scene[0]["token"]
+        cls.scene_name = nusc.scene[0]["name"]
+
+        config = NuScenesConverter4Config(
+            root_dir=cls.nuscenes_dir,
+            output_dir=cls.output_dir,
+            no_cameras=False,
+            camera_ids=None,
+            no_lidars=False,
+            lidar_ids=None,
+            no_radars=False,
+            radar_ids=None,
+            verbose=False,
+            debug=False,
+            debug_port=5678,
+            version=cls.nuscenes_version,
+            scene_token=cls.scene_token,
+            scene_name=None,
+            store_type=cls.store_type,
+            component_group_profile="separate-sensors",
+            store_sequence_meta=True,
+        )
+        NuScenesConverter4.convert(config)
+
+        # Find output sequence directory (named after scene_name)
+        seq_dirs = [d for d in UPath(cls.output_dir).iterdir() if d.is_dir()]
+        assert len(seq_dirs) == 1, f"Expected 1 sequence dir, found {len(seq_dirs)}: {seq_dirs}"
+        cls.seq_dir = seq_dirs[0]
+
+        # Open reader via the sequence meta JSON file
+        meta_files = list(cls.seq_dir.glob("*.json"))
+        assert len(meta_files) == 1, f"Expected 1 meta JSON, found {len(meta_files)}"
+        cls.reader = SequenceComponentGroupsReader([meta_files[0]])
+
+    @classmethod
+    def tearDownClass(cls):
+        cls._tempdir.cleanup()
+
+    # --- Poses ----------------------------------------------------------------
+
+    def test_sequence_has_dynamic_rig_to_world_pose(self):
+        """Verify dynamic rig -> world ego pose exists."""
+        poses_readers = self.reader.open_component_readers(PosesComponent.Reader)
+        self.assertEqual(len(poses_readers), 1)
+        poses_reader = list(poses_readers.values())[0]
+
+        poses, timestamps = poses_reader.get_dynamic_pose("rig", "world")
+        self.assertEqual(poses.shape[1:], (4, 4))
+        self.assertGreater(poses.shape[0], 0)
+        self.assertEqual(timestamps.shape[0], poses.shape[0])
+
+    def test_sequence_has_static_world_to_world_global(self):
+        """Verify static world -> world_global pose exists."""
+        poses_readers = self.reader.open_component_readers(PosesComponent.Reader)
+        poses_reader = list(poses_readers.values())[0]
+
+        static_poses = dict(poses_reader.get_static_poses())
+        self.assertIn(("world", "world_global"), static_poses)
+        pose = static_poses[("world", "world_global")]
+        self.assertEqual(pose.shape, (4, 4))
+
+    def test_first_pose_near_identity(self):
+        """Verify the first actual sweep pose is near identity (local origin)."""
+        poses_readers = self.reader.open_component_readers(PosesComponent.Reader)
+        poses_reader = list(poses_readers.values())[0]
+
+        poses, _ = poses_reader.get_dynamic_pose("rig", "world")
+        # poses[0] is the extrapolated boundary; poses[1] is the first real sweep
+        second_pose = poses[1]
+        np.testing.assert_array_almost_equal(second_pose, np.eye(4, dtype=np.float32), decimal=3)
+
+    # --- Cameras --------------------------------------------------------------
+
+    def test_six_cameras_exist(self):
+        """Verify all 6 camera readers exist with frames."""
+        camera_readers = self.reader.open_component_readers(CameraSensorComponent.Reader)
+        expected_ids = {
+            "camera_front",
+            "camera_front_left",
+            "camera_front_right",
+            "camera_back",
+            "camera_back_left",
+            "camera_back_right",
+        }
+        self.assertEqual(set(camera_readers.keys()), expected_ids)
+        for cam_id, cam_reader in camera_readers.items():
+            self.assertGreater(cam_reader.frames_count, 0, f"{cam_id} should have frames")
+
+    def test_camera_intrinsics_zero_distortion(self):
+        """Verify camera intrinsics have zero distortion (undistorted images)."""
+        intrinsics_readers = self.reader.open_component_readers(IntrinsicsComponent.Reader)
+        self.assertEqual(len(intrinsics_readers), 1)
+        intrinsics_reader = list(intrinsics_readers.values())[0]
+
+        for cam_id in [
+            "camera_front",
+            "camera_front_left",
+            "camera_front_right",
+            "camera_back",
+            "camera_back_left",
+            "camera_back_right",
+        ]:
+            params = intrinsics_reader.get_camera_model_parameters(cam_id)
+            self.assertIsInstance(params, OpenCVPinholeCameraModelParameters)
+            params = cast(OpenCVPinholeCameraModelParameters, params)
+            np.testing.assert_array_equal(params.radial_coeffs, np.zeros(6, dtype=np.float32))
+            np.testing.assert_array_equal(params.tangential_coeffs, np.zeros(2, dtype=np.float32))
+            self.assertTrue(np.all(params.focal_length > 0))
+
+    def test_camera_extrinsics_stored_as_static_poses(self):
+        """Verify each camera has a static sensor -> rig extrinsic pose."""
+        poses_readers = self.reader.open_component_readers(PosesComponent.Reader)
+        poses_reader = list(poses_readers.values())[0]
+
+        static_poses = dict(poses_reader.get_static_poses())
+        for cam_id in [
+            "camera_front",
+            "camera_front_left",
+            "camera_front_right",
+            "camera_back",
+            "camera_back_left",
+            "camera_back_right",
+        ]:
+            self.assertIn((cam_id, "rig"), static_poses, f"Missing static pose for {cam_id}")
+            pose = static_poses[(cam_id, "rig")]
+            self.assertEqual(pose.shape, (4, 4))
+
+    # --- Lidar ----------------------------------------------------------------
+
+    def test_lidar_exists_with_frames(self):
+        """Verify lidar reader exists with frames."""
+        lidar_readers = self.reader.open_component_readers(LidarSensorComponent.Reader)
+        self.assertIn("lidar_top", lidar_readers)
+        lidar_reader = lidar_readers["lidar_top"]
+        self.assertGreater(lidar_reader.frames_count, 0)
+
+    def test_lidar_extrinsic_stored_as_static_pose(self):
+        """Verify lidar has a static sensor -> rig extrinsic pose."""
+        poses_readers = self.reader.open_component_readers(PosesComponent.Reader)
+        poses_reader = list(poses_readers.values())[0]
+
+        static_poses = dict(poses_reader.get_static_poses())
+        self.assertIn(("lidar_top", "rig"), static_poses)
+        pose = static_poses[("lidar_top", "rig")]
+        self.assertEqual(pose.shape, (4, 4))
+
+    def test_lidar_has_structured_model(self):
+        """Verify lidar intrinsics contain a structured spinning model."""
+
+        intrinsics_readers = self.reader.open_component_readers(IntrinsicsComponent.Reader)
+        intrinsics_reader = list(intrinsics_readers.values())[0]
+
+        params = intrinsics_reader.get_lidar_model_parameters("lidar_top")
+        self.assertIsNotNone(params)
+        self.assertIsInstance(params, RowOffsetStructuredSpinningLidarModelParameters)
+        assert isinstance(params, RowOffsetStructuredSpinningLidarModelParameters)  # narrow type
+
+        # HDL-32E: 32 rows, spinning CW at ~20Hz
+        self.assertEqual(params.n_rows, 32)
+        self.assertGreater(params.n_columns, 1000)  # typically ~1084
+        self.assertEqual(params.spinning_direction, "cw")
+        self.assertAlmostEqual(params.spinning_frequency_hz, 20.0, delta=1.0)
+
+        # Elevation angles: should span roughly -30 to +10 deg for HDL-32E
+        self.assertEqual(len(params.row_elevations_rad), 32)
+        min_elev_deg = np.degrees(params.row_elevations_rad.min())
+        max_elev_deg = np.degrees(params.row_elevations_rad.max())
+        self.assertLess(min_elev_deg, -20.0)
+        self.assertGreater(max_elev_deg, 5.0)
+
+        # Column azimuths: should span nearly 360 degrees
+        azimuth_span = params.column_azimuths_rad.max() - params.column_azimuths_rad.min()
+        self.assertGreater(np.degrees(azimuth_span), 300.0)
+
+    def test_lidar_model_reproduces_point_cloud(self):
+        """Verify that model-based points match native direction*distance points across frames.
+
+        For each lidar frame, computes points two ways:
+        1. Native: direction * distance (stored ray-bundle data)
+        2. Model-based: elements_to_sensor_points(model_element, distance)
+
+        The model is derived from median statistics, so we expect small angular
+        deviations (~0.1 deg) but the overall structure should match closely.
+        """
+
+        intrinsics_readers = self.reader.open_component_readers(IntrinsicsComponent.Reader)
+        intrinsics_reader = list(intrinsics_readers.values())[0]
+        params = intrinsics_reader.get_lidar_model_parameters("lidar_top")
+        assert isinstance(params, RowOffsetStructuredSpinningLidarModelParameters)
+
+        lidar_model = StructuredLidarModel.maybe_from_parameters(params, device="cpu")
+        assert lidar_model is not None
+
+        lidar_readers = self.reader.open_component_readers(LidarSensorComponent.Reader)
+        lidar_reader = lidar_readers["lidar_top"]
+
+        # Get frame end timestamps for indexing (reader API uses end-of-frame timestamp as key)
+        frame_timestamps = lidar_reader.frames_timestamps_us  # [N, 2] (start, end)
+        frame_end_timestamps = frame_timestamps[:, 1]
+        n_frames = len(frame_end_timestamps)
+
+        # Check a subset of frames (every 20th to keep test fast)
+        frame_indices = list(range(0, n_frames, max(1, n_frames // 20)))
+
+        max_angular_errors_deg = []
+        mean_angular_errors_deg = []
+
+        for idx in frame_indices:
+            ts = int(frame_end_timestamps[idx])
+
+            # Native: direction * distance
+            direction = lidar_reader.get_frame_ray_bundle_data(ts, "direction")
+            distance_2d = np.array(lidar_reader._get_ray_bundle_returns_group(ts)["distance_m"])  # [R, N]
+            distance = distance_2d[0]  # first return
+
+            # Model element
+            model_element_data = lidar_reader.get_frame_ray_bundle_data(ts, "model_element")
+
+            # Filter valid (finite, positive distance)
+            valid = np.isfinite(distance) & (distance > 0)
+            if not valid.any():
+                continue
+
+            native_pts = direction[valid] * distance[valid, np.newaxis]
+
+            # Model-based: elements_to_sensor_points
+            model_pts = (
+                lidar_model.elements_to_sensor_points(
+                    model_element_data[valid],
+                    distance[valid],
+                )
+                .cpu()
+                .numpy()
+            )
+
+            # Compare via angular error between the two point sets
+            native_norms = np.linalg.norm(native_pts, axis=1, keepdims=True)
+            model_norms = np.linalg.norm(model_pts, axis=1, keepdims=True)
+            native_dirs = native_pts / np.maximum(native_norms, 1e-8)
+            model_dirs = model_pts / np.maximum(model_norms, 1e-8)
+            cos_angle = np.clip(np.sum(native_dirs * model_dirs, axis=1), -1.0, 1.0)
+            angular_error_deg = np.degrees(np.arccos(cos_angle))
+
+            max_angular_errors_deg.append(float(angular_error_deg.max()))
+            mean_angular_errors_deg.append(float(angular_error_deg.mean()))
+
+        # Expect: mean angular error < 0.5 deg overall, < 0.2 deg for >20m range.
+        # The model uses a linear fit of far-range azimuths + iterative timestamp refinement.
+        # Deviations come from MC distortion (translational) which scales inversely with range.
+        overall_mean = np.mean(mean_angular_errors_deg)
+        overall_max = np.max(max_angular_errors_deg)
+        self.assertLess(overall_mean, 0.5, f"Mean angular error too large: {overall_mean:.3f} deg")
+        self.assertLess(overall_max, 5.0, f"Max angular error too large: {overall_max:.3f} deg")
+
+    # --- Radars ---------------------------------------------------------------
+
+    def test_five_radars_exist(self):
+        """Verify all 5 radar readers exist with frames."""
+        radar_readers = self.reader.open_component_readers(RadarSensorComponent.Reader)
+        expected_ids = {
+            "radar_front",
+            "radar_front_left",
+            "radar_front_right",
+            "radar_back_left",
+            "radar_back_right",
+        }
+        self.assertEqual(set(radar_readers.keys()), expected_ids)
+        for radar_id, radar_reader in radar_readers.items():
+            self.assertGreater(radar_reader.frames_count, 0, f"{radar_id} should have frames")
+
+    def test_radar_extrinsics_stored_as_static_poses(self):
+        """Verify each radar has a static sensor -> rig extrinsic pose."""
+        poses_readers = self.reader.open_component_readers(PosesComponent.Reader)
+        poses_reader = list(poses_readers.values())[0]
+
+        static_poses = dict(poses_reader.get_static_poses())
+        for radar_id in [
+            "radar_front",
+            "radar_front_left",
+            "radar_front_right",
+            "radar_back_left",
+            "radar_back_right",
+        ]:
+            self.assertIn((radar_id, "rig"), static_poses, f"Missing static pose for {radar_id}")
+            pose = static_poses[(radar_id, "rig")]
+            self.assertEqual(pose.shape, (4, 4))
+
+    # --- Cuboids (Annotations) ------------------------------------------------
+
+    def test_cuboid_observations_exist(self):
+        """Verify cuboid track observations were stored from annotations."""
+        cuboid_readers = self.reader.open_component_readers(CuboidsComponent.Reader)
+        # v1.0-mini has annotations, v1.0-test does not
+        if not cuboid_readers:
+            self.skipTest("No cuboid component (possibly test split with no annotations)")
+
+        self.assertEqual(len(cuboid_readers), 1)
+        cuboid_reader = list(cuboid_readers.values())[0]
+
+        observations = list(cuboid_reader.get_observations())
+        self.assertGreater(len(observations), 0)
+
+        # Check first observation has expected fields
+        obs = observations[0]
+        self.assertIsInstance(obs.track_id, str)
+        self.assertIsInstance(obs.class_id, str)
+        self.assertEqual(obs.reference_frame_id, "world_global")
+
+    # --- Sequence Meta --------------------------------------------------------
+
+    def test_sequence_meta_file_exists(self):
+        """Verify sequence meta JSON file was written."""
+        meta_files = list(self.seq_dir.glob("*.json"))
+        self.assertEqual(len(meta_files), 1)
+
+    def test_sequence_id_matches_scene_name(self):
+        """Verify the sequence ID matches the scene name."""
+        self.assertEqual(self.reader.sequence_id, self.scene_name)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tools/data_converter/nuscenes/lidar_model.py b/tools/data_converter/nuscenes/lidar_model.py
new file mode 100644
index 00000000..962aabf0
--- /dev/null
+++ b/tools/data_converter/nuscenes/lidar_model.py
@@ -0,0 +1,739 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Modular lidar model utilities for structured spinning lidar processing.
+
+This module provides pure, independently testable functions for:
+- Column alignment between measured lidar spins and a static model
+- Per-column azimuth extraction from point clouds
+- Frame timestamp computation from model column indices
+- Motion decompensation of MC'd points
+- Model parameter derivation from decompensated data
+- Iterative frame alignment pipeline
+- Multi-frame model optimization
+- Model consistency metrics
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Literal, Optional
+
+import numpy as np
+
+from ncore.impl.common.transformations import MotionCompensator
+from ncore.impl.data.types import RowOffsetStructuredSpinningLidarModelParameters
+
+
+# --- Constants -----------------------------------------------------------------
+
+HDL32E_N_BEAMS: int = 32
+HDL32E_N_TARGET_COLS: int = 1085
+HDL32E_SCAN_DURATION_US: int = 50_000
+
+
+# --- Internal helpers ----------------------------------------------------------
+
+
+def _grouped_median(values: np.ndarray, groups: np.ndarray, n_groups: int, min_count: int = 3) -> np.ndarray:
+    """Compute median of values grouped by integer group index (vectorized).
+
+    Args:
+        values: Values to aggregate [N].
+        groups: Group index per value [N], integers in [0, n_groups).
+        n_groups: Total number of groups.
+        min_count: Minimum values per group to produce a valid median.
+
+    Returns:
+        Per-group medians [n_groups]. Groups with fewer than min_count values get 0.
+    """
+    result = np.zeros(n_groups, dtype=np.float64)
+    sort_idx = np.argsort(groups)
+    sorted_groups = groups[sort_idx]
+    sorted_values = values[sort_idx]
+    # Find group boundaries using searchsorted
+    boundaries = np.searchsorted(sorted_groups, np.arange(n_groups + 1))
+    for g in range(n_groups):
+        start, end = boundaries[g], boundaries[g + 1]
+        if end - start >= min_count:
+            result[g] = np.median(sorted_values[start:end])
+    return result
+
+
+# --- Data structures -----------------------------------------------------------
+
+
+@dataclass
+class LidarColumnAlignment:
+    """Result of column alignment between a lidar spin and a static model."""
+
+    spin_column_range: range
+    static_column_range: range
+    mean_alignment_error_rad: float
+
+
+@dataclass
+class LidarFrameData:
+    """Processed lidar frame data ready for storage."""
+
+    xyz_decompensated: np.ndarray  # [N, 3] decompensated points
+    intensity: np.ndarray  # [N] normalized intensity
+    timestamps_us: np.ndarray  # [N] uint64 per-point timestamps
+    model_element: np.ndarray  # [N, 2] uint16 (model_row, model_col)
+    frame_start_us: int
+    frame_end_us: int
+
+
+# --- Core functions ------------------------------------------------------------
+
+
+def compute_column_alignment(
+    spin_azimuths_rad: np.ndarray,
+    model_column_azimuths_rad: np.ndarray,
+    max_column_shift: int = 20,
+    resolution_factor: int = 1,
+) -> LidarColumnAlignment:
+    """Align measured column azimuths to a static model via brute-force shift search.
+
+    Tries integer column shifts in [-max_column_shift, +max_column_shift] and selects
+    the shift minimizing the mean angular difference. Uses arccos(cos(...)) for
+    wrap-around safety.
+
+    When resolution_factor > 1, both arrays are upsampled by linear interpolation
+    before alignment, giving sub-column shift precision (e.g., factor=4 gives
+    quarter-column granularity). The returned ranges are in original resolution.
+
+    Args:
+        spin_azimuths_rad: Per-column measured azimuths for the current frame [n_spin_cols].
+        model_column_azimuths_rad: Static model column azimuths [n_model_cols].
+        max_column_shift: Maximum shift in original columns to search.
+        resolution_factor: Upsample factor for sub-column precision (1 = native).
+
+    Returns:
+        LidarColumnAlignment with optimal spin/static column ranges (in original resolution).
+    """
+    # Upsample if requested
+    if resolution_factor > 1:
+        n_spin_orig = len(spin_azimuths_rad)
+        n_model_orig = len(model_column_azimuths_rad)
+
+        # Upsample spin azimuths (interpolate, handling NaN gaps)
+        valid_spin = ~np.isnan(spin_azimuths_rad)
+        if valid_spin.all():
+            spin_unwrapped = np.unwrap(spin_azimuths_rad)
+        else:
+            vi = np.where(valid_spin)[0]
+            spin_unwrapped = np.interp(np.arange(n_spin_orig), vi, np.unwrap(spin_azimuths_rad[valid_spin]))
+
+        spin_up = np.interp(
+            np.linspace(0, n_spin_orig - 1, n_spin_orig * resolution_factor),
+            np.arange(n_spin_orig),
+            spin_unwrapped,
+        )
+        spin_up = (spin_up + np.pi) % (2 * np.pi) - np.pi
+
+        # Upsample model azimuths
+        model_unwrapped = np.unwrap(model_column_azimuths_rad.astype(np.float64))
+        model_up = np.interp(
+            np.linspace(0, n_model_orig - 1, n_model_orig * resolution_factor),
+            np.arange(n_model_orig),
+            model_unwrapped,
+        )
+        model_up = ((model_up + np.pi) % (2 * np.pi) - np.pi).astype(np.float32)
+
+        # Search at upsampled resolution
+        max_shift_up = max_column_shift * resolution_factor
+        result_up = compute_column_alignment(spin_up, model_up, max_shift_up, resolution_factor=1)
+
+        # Convert ranges back to original resolution (floor division for start, ceil for stop)
+        spin_start = result_up.spin_column_range.start // resolution_factor
+        spin_stop = (result_up.spin_column_range.stop + resolution_factor - 1) // resolution_factor
+        spin_stop = min(spin_stop, n_spin_orig)
+        static_start = result_up.static_column_range.start // resolution_factor
+        static_stop = static_start + (spin_stop - spin_start)
+        static_stop = min(static_stop, n_model_orig)
+        # Ensure equal lengths
+        length = min(spin_stop - spin_start, static_stop - static_start)
+
+        return LidarColumnAlignment(
+            spin_column_range=range(spin_start, spin_start + length),
+            static_column_range=range(static_start, static_start + length),
+            mean_alignment_error_rad=result_up.mean_alignment_error_rad,
+        )
+
+    # Native resolution search
+    best_spin_range = range(0, 0)
+    best_static_range = range(0, 0)
+    best_mean_error: Optional[float] = None
+
+    n_spin = len(spin_azimuths_rad)
+    n_model = len(model_column_azimuths_rad)
+
+    for shift in range(-max_column_shift, max_column_shift + 1):
+        spin_start = max(-shift, 0)
+        static_start = max(shift, 0)
+        spin_stop = min(n_spin, n_model - static_start + spin_start)
+        static_stop = static_start + (spin_stop - spin_start)
+
+        if spin_stop <= spin_start:
+            continue
+
+        # cos-based angular difference (handles wrap-around)
+        abs_error_rad = np.arccos(
+            np.clip(
+                np.cos(spin_azimuths_rad[spin_start:spin_stop] - model_column_azimuths_rad[static_start:static_stop]),
+                -1.0,
+                1.0,
+            )
+        )
+
+        mean_err = float(abs_error_rad.mean())
+
+        if best_mean_error is None or mean_err < best_mean_error:
+            best_mean_error = mean_err
+            best_spin_range = range(spin_start, spin_stop)
+            best_static_range = range(static_start, static_stop)
+
+    assert best_mean_error is not None
+
+    return LidarColumnAlignment(
+        spin_column_range=best_spin_range,
+        static_column_range=best_static_range,
+        mean_alignment_error_rad=best_mean_error,
+    )
+
+
+def extract_column_azimuths(
+    xyz: np.ndarray,
+    col_idx: np.ndarray,
+    n_cols: int,
+    min_range_m: float = 20.0,
+    min_points_per_col: int = 3,
+) -> np.ndarray:
+    """Extract per-column median azimuths from far-range points.
+
+    Args:
+        xyz: Point cloud [N, 3].
+        col_idx: Column index per point [N].
+        n_cols: Total number of columns.
+        min_range_m: Minimum point range to include.
+        min_points_per_col: Minimum points needed for a valid column azimuth.
+
+    Returns:
+        Per-column azimuths [n_cols], with NaN for columns without enough data.
+    """
+    dist = np.linalg.norm(xyz, axis=1)
+    azimuth = np.arctan2(xyz[:, 1], xyz[:, 0])
+    col_az = np.full(n_cols, np.nan, dtype=np.float64)
+
+    valid = dist > min_range_m
+    valid_az = azimuth[valid]
+    valid_cols = col_idx[valid]
+
+    # Vectorized per-column median using sorted groups
+    if len(valid_az) > 0:
+        sort_idx = np.argsort(valid_cols)
+        sorted_cols = valid_cols[sort_idx]
+        sorted_az = valid_az[sort_idx]
+        # Find group boundaries
+        col_changes = np.concatenate([[0], np.where(np.diff(sorted_cols))[0] + 1, [len(sorted_cols)]])
+        for gi in range(len(col_changes) - 1):
+            start, end = col_changes[gi], col_changes[gi + 1]
+            if end - start >= min_points_per_col:
+                c = sorted_cols[start]
+                if c < n_cols:
+                    col_az[c] = np.median(sorted_az[start:end])
+
+    return col_az
+
+
+def compute_frame_timestamps(
+    model_col: np.ndarray,
+    n_model_cols: int,
+    frame_start_us: int,
+    frame_end_us: int,
+) -> np.ndarray:
+    """Compute per-point timestamps from model column indices.
+
+    Each column corresponds to a fixed fraction of the rotation.
+    Column k fires at: frame_start + k/n_model_cols * duration.
+
+    Args:
+        model_col: Model column index per point [N], integer array.
+        n_model_cols: Total number of model columns.
+        frame_start_us: Frame start timestamp in microseconds.
+        frame_end_us: Frame end timestamp in microseconds.
+
+    Returns:
+        Per-point timestamps [N] as uint64, clipped to [frame_start_us, frame_end_us].
+    """
+    fraction = model_col.astype(np.float64) / n_model_cols
+    duration_us = frame_end_us - frame_start_us
+    timestamps = frame_start_us + fraction * duration_us
+    timestamps = np.clip(timestamps, frame_start_us, frame_end_us)
+    return timestamps.astype(np.uint64)
+
+
+def decompensate_frame(
+    xyz_mc: np.ndarray,
+    timestamps_us: np.ndarray,
+    frame_start_us: int,
+    frame_end_us: int,
+    motion_compensator: MotionCompensator,
+    sensor_id: str,
+) -> np.ndarray:
+    """Decompensate MC'd points to raw sensor-at-measurement-time frame.
+
+    Args:
+        xyz_mc: Motion-compensated points [N, 3] float32.
+        timestamps_us: Per-point timestamps [N] uint64.
+        frame_start_us: Frame start timestamp in microseconds.
+        frame_end_us: Frame end timestamp in microseconds.
+        motion_compensator: MotionCompensator instance with loaded poses.
+        sensor_id: Sensor identifier for pose lookup.
+
+    Returns:
+        Decompensated points [N, 3] in the time-dependent sensor frame.
+    """
+    return motion_compensator.motion_decompensate_points(
+        sensor_id=sensor_id,
+        xyz_sensorend=xyz_mc,
+        timestamp_us=timestamps_us,
+        frame_start_timestamp_us=frame_start_us,
+        frame_end_timestamp_us=frame_end_us,
+    )
+
+
+def derive_model_from_decompensated(
+    xyz_decompensated: np.ndarray,
+    n_beams_per_column: int,
+    n_target_cols: int,
+    spinning_direction: Literal["cw", "ccw"],
+    spinning_frequency_hz: float,
+    min_valid_distance_m: float = 0.5,
+) -> Optional[RowOffsetStructuredSpinningLidarModelParameters]:
+    """Derive structured lidar model directly from decompensated point cloud.
+
+    Uses the same approach as extracting parameters from raw sensor data:
+    - column_azimuths from a reference row's per-column azimuths
+    - row_azimuth_offsets from the center column (each row vs reference row)
+    - row_elevations from the center column
+
+    Points below min_valid_distance_m are treated as "no return" sentinel values
+    (the HDL-32E stores ~0.1m vectors for missed returns) and excluded.
+
+    Args:
+        xyz_decompensated: Decompensated (raw) point cloud [N, 3].
+        n_beams_per_column: Beams per column (32 for HDL-32E).
+        n_target_cols: Expected column count. Returns None if different.
+        spinning_direction: "cw" or "ccw".
+        spinning_frequency_hz: Spinning frequency in Hz.
+        min_valid_distance_m: Minimum distance to consider a point as a valid return.
+
+    Returns:
+        Model parameters, or None if frame doesn't match target column count.
+    """
+    n_points = len(xyz_decompensated)
+    n_cols = n_points // n_beams_per_column
+    if n_cols != n_target_cols:
+        return None
+
+    # Reshape into [n_cols, n_beams_per_column, 3] grid
+    xyz_grid = xyz_decompensated.reshape(n_cols, n_beams_per_column, 3)
+
+    # Compute per-cell angles and distances from the grid
+    dist_grid = np.linalg.norm(xyz_grid, axis=2)
+    az_grid = np.arctan2(xyz_grid[:, :, 1], xyz_grid[:, :, 0])
+    xy_range_grid = np.sqrt(xyz_grid[:, :, 0] ** 2 + xyz_grid[:, :, 1] ** 2)
+    el_grid = np.arctan2(xyz_grid[:, :, 2], xy_range_grid)
+
+    # Valid mask: exclude "no return" sentinel points (stored as ~0.1m vectors)
+    valid_grid = dist_grid > min_valid_distance_m
+
+    # Reference row: the one with the most valid returns across all columns
+    row_valid_count = valid_grid.sum(axis=0)
+    ref_row = int(np.argmax(row_valid_count))
+
+    # column_azimuths: reference row's per-column azimuths (monotonic after decompensation).
+    # For columns where the reference row has a sentinel, interpolate from valid neighbors.
+    ref_valid = valid_grid[:, ref_row]
+    if ref_valid.sum() < n_cols * 0.9:
+        # Not enough valid data in the reference row -- frame is unsuitable
+        return None
+
+    col_az = az_grid[:, ref_row].astype(np.float64)
+    if not ref_valid.all():
+        valid_indices = np.where(ref_valid)[0]
+        valid_az_unwrapped = np.unwrap(col_az[ref_valid])
+        col_az = np.interp(np.arange(n_cols, dtype=np.float64), valid_indices, valid_az_unwrapped)
+
+    column_azimuths_rad = col_az.astype(np.float32)
+    # Normalize wrap-around: force strictly decreasing for CW
+    column_azimuths_rad[column_azimuths_rad > column_azimuths_rad[0]] -= np.float32(2 * np.pi)
+
+    # row_azimuth_offsets: each row's azimuth offset relative to reference row.
+    # Start with a small window around center column; progressively widen if a ring
+    # has no valid returns there (lower beams often only produce sentinels near center).
+    center_col = n_cols // 2
+    row_azimuth_offsets = np.zeros(n_beams_per_column, dtype=np.float32)
+
+    # Compute azimuth difference grid: az_grid - column_azimuths (broadcast)
+    az_diff_grid = np.arctan2(
+        np.sin(az_grid - column_azimuths_rad[:, np.newaxis]),
+        np.cos(az_grid - column_azimuths_rad[:, np.newaxis]),
+    )  # [n_cols, n_beams]
+
+    # For each row, find valid diffs in progressively wider windows around center
+    for r in range(n_beams_per_column):
+        for half_width in [5, 50, n_cols // 2]:
+            window_start = max(0, center_col - half_width)
+            window_stop = min(n_cols, center_col + half_width + 1)
+            window_valid = valid_grid[window_start:window_stop, r]
+            if window_valid.any():
+                row_azimuth_offsets[r] = np.median(az_diff_grid[window_start:window_stop, r][window_valid])
+                break
+
+    # row_elevations: median across all valid columns per ring (vectorized)
+    row_elevations = np.zeros(n_beams_per_column, dtype=np.float32)
+    for r in range(n_beams_per_column):
+        valid_cols_r = valid_grid[:, r]
+        if valid_cols_r.any():
+            row_elevations[r] = np.median(el_grid[valid_cols_r, r])
+
+    # Reverse row order: model expects row 0 = highest elevation (ring n_beams-1)
+    row_azimuth_offsets = row_azimuth_offsets[::-1]
+    row_elevations = row_elevations[::-1]
+
+    return RowOffsetStructuredSpinningLidarModelParameters(
+        spinning_frequency_hz=spinning_frequency_hz,
+        spinning_direction=spinning_direction,
+        n_rows=n_beams_per_column,
+        n_columns=n_cols,
+        row_elevations_rad=row_elevations,
+        column_azimuths_rad=column_azimuths_rad,
+        row_azimuth_offsets_rad=row_azimuth_offsets,
+    )
+
+
+def align_frame(
+    xyz_mc: np.ndarray,
+    ring_index: np.ndarray,
+    intensity: np.ndarray,
+    n_beams_per_column: int,
+    model_params: RowOffsetStructuredSpinningLidarModelParameters,
+    motion_compensator: MotionCompensator,
+    sensor_id: str,
+    frame_start_us: int,
+    frame_end_us: int,
+    n_iterations: int = 2,
+    min_valid_distance_m: float = 0.5,
+    alignment_resolution_factor: int = 1,
+) -> Optional[LidarFrameData]:
+    """Align a single frame to the model and decompensate.
+
+    Iteratively:
+    1. Align columns (MC'd azimuths on first iter, decompensated on subsequent)
+    2. Compute timestamps from alignment
+    3. Decompensate
+
+    Returns processed frame data ready for storage.
+
+    Args:
+        xyz_mc: Motion-compensated point cloud [N, 3] float32.
+        ring_index: Beam ID per point [N], values 0..n_beams_per_column-1.
+        n_beams_per_column: Number of beams per firing column.
+        model_params: Static lidar model parameters.
+        motion_compensator: MotionCompensator instance with loaded poses.
+        sensor_id: Sensor identifier for pose lookup.
+        frame_start_us: Frame start timestamp in microseconds.
+        frame_end_us: Frame end timestamp in microseconds.
+        n_iterations: Number of align-decompensate iterations.
+        min_valid_distance_m: Minimum distance to consider a point valid.
+
+    Returns:
+        LidarFrameData with filtered, aligned, and decompensated frame data.
+    """
+    n_points = len(xyz_mc)
+    n_cols = n_points // n_beams_per_column
+    n_model_cols = model_params.n_columns
+    col_idx = np.arange(n_points, dtype=np.int64) // n_beams_per_column
+
+    xyz_decomp_full: Optional[np.ndarray] = None
+    alignment: Optional[LidarColumnAlignment] = None
+    spin_range: Optional[range] = None
+
+    for iteration in range(n_iterations):
+        if iteration == 0:
+            # Initial alignment from MC'd azimuths (approximate)
+            spin_col_azimuths = extract_column_azimuths(xyz_mc, col_idx, n_cols, min_range_m=20.0)
+            valid_az_mask = ~np.isnan(spin_col_azimuths)
+            if valid_az_mask.sum() < n_cols * 0.3:
+                spin_col_azimuths = extract_column_azimuths(xyz_mc, col_idx, n_cols, min_range_m=5.0)
+                valid_az_mask = ~np.isnan(spin_col_azimuths)
+        else:
+            # Refined alignment from decompensated azimuths (accurate).
+            # Only use columns within the overlap range -- boundary columns have
+            # incorrect decompensation due to clamped timestamps.
+            assert xyz_decomp_full is not None
+            assert spin_range is not None
+            spin_col_azimuths = extract_column_azimuths(xyz_decomp_full, col_idx, n_cols, min_range_m=0.5)
+            # Invalidate columns outside the previous iteration's overlap
+            spin_col_azimuths[: spin_range.start] = np.nan
+            spin_col_azimuths[spin_range.stop :] = np.nan
+            valid_az_mask = ~np.isnan(spin_col_azimuths)
+
+        # Fill NaN gaps by interpolation
+        if valid_az_mask.any() and not valid_az_mask.all():
+            valid_indices = np.where(valid_az_mask)[0]
+            valid_values = spin_col_azimuths[valid_az_mask]
+            valid_unwrapped = np.unwrap(valid_values)
+            all_unwrapped = np.interp(np.arange(n_cols), valid_indices, valid_unwrapped)
+            spin_col_azimuths = ((all_unwrapped + np.pi) % (2 * np.pi) - np.pi).astype(np.float64)
+
+        alignment = compute_column_alignment(
+            spin_azimuths_rad=spin_col_azimuths,
+            model_column_azimuths_rad=model_params.column_azimuths_rad,
+            max_column_shift=20,
+            resolution_factor=alignment_resolution_factor,
+        )
+
+        # Compute timestamps from model column indices (only for overlap points)
+        spin_range = alignment.spin_column_range
+        model_col_full = (alignment.static_column_range.start + (col_idx - spin_range.start)).astype(np.int64)
+
+        # For points outside the overlap, assign the nearest boundary timestamp.
+        # These points will be filtered before storage, but need valid timestamps
+        # for the full-frame decompensation used in the next alignment iteration.
+        model_col_full[col_idx < spin_range.start] = 0
+        model_col_full[col_idx >= spin_range.stop] = n_model_cols - 1
+
+        point_timestamps_full = compute_frame_timestamps(model_col_full, n_model_cols, frame_start_us, frame_end_us)
+
+        # Decompensate full frame (needed for refined alignment on next iteration)
+        xyz_decomp_full = decompensate_frame(
+            xyz_mc=xyz_mc,
+            timestamps_us=point_timestamps_full,
+            frame_start_us=frame_start_us,
+            frame_end_us=frame_end_us,
+            motion_compensator=motion_compensator,
+            sensor_id=sensor_id,
+        )
+
+    # --- Apply final alignment: keep only points with valid model element indices ---
+    assert alignment is not None
+    assert spin_range is not None
+    assert xyz_decomp_full is not None
+
+    in_overlap = (col_idx >= spin_range.start) & (col_idx < spin_range.stop)
+    distance_mc = np.linalg.norm(xyz_mc, axis=1)
+    valid_mask = in_overlap & (distance_mc > min_valid_distance_m)
+
+    # Model element indices
+    col_idx_filtered = col_idx[valid_mask]
+    ring_index_filtered = ring_index[valid_mask]
+    model_row = (n_beams_per_column - 1 - ring_index_filtered).astype(np.uint16)
+    model_col = (alignment.static_column_range.start + (col_idx_filtered - spin_range.start)).astype(np.uint16)
+    model_element = np.stack([model_row, model_col], axis=1)
+
+    # Final timestamps from model column
+    timestamps_us = compute_frame_timestamps(model_col, n_model_cols, frame_start_us, frame_end_us)
+
+    # Final decompensation of filtered points
+    xyz_decompensated = decompensate_frame(
+        xyz_mc=xyz_mc[valid_mask],
+        timestamps_us=timestamps_us,
+        frame_start_us=frame_start_us,
+        frame_end_us=frame_end_us,
+        motion_compensator=motion_compensator,
+        sensor_id=sensor_id,
+    )
+
+    return LidarFrameData(
+        xyz_decompensated=xyz_decompensated,
+        intensity=intensity[valid_mask],
+        timestamps_us=timestamps_us,
+        model_element=model_element,
+        frame_start_us=frame_start_us,
+        frame_end_us=frame_end_us,
+    )
+
+
+def optimize_model(
+    model_params: RowOffsetStructuredSpinningLidarModelParameters,
+    frame_azimuths: list[np.ndarray],
+    frame_model_cols: list[np.ndarray],
+    frame_model_rows: list[np.ndarray],
+    frame_distances: list[np.ndarray],
+    min_range_m: float = 10.0,
+    n_iterations: int = 1,
+) -> RowOffsetStructuredSpinningLidarModelParameters:
+    """Optimize model parameters from multi-frame observations.
+
+    Solves for column_azimuths and row_azimuth_offsets that minimize the
+    angular error across all frames, weighted by distance (far-range points
+    have less MC distortion).
+
+    Each iteration:
+    1. Compute residuals: actual_azimuth - (column_azimuths[col] + row_offsets[row])
+    2. Update column_azimuths: median residual per column across all frames
+    3. Update row_offsets: median residual per row across all frames
+
+    Parameters:
+        model_params: Initial model to refine.
+        frame_azimuths: Decompensated azimuth per point for each frame.
+        frame_model_cols: Model column index per point for each frame.
+        frame_model_rows: Model row index per point for each frame.
+        frame_distances: Distance per point for each frame (for weighting).
+        min_range_m: Minimum distance for points to include in optimization.
+        n_iterations: Number of alternating optimization iterations.
+
+    Returns:
+        Optimized model parameters.
+    """
+    n_columns = model_params.n_columns
+    n_rows = model_params.n_rows
+
+    # Work in float64 for precision
+    column_azimuths = model_params.column_azimuths_rad.astype(np.float64)
+    row_offsets = model_params.row_azimuth_offsets_rad.astype(np.float64)
+
+    # Concatenate all frames, filtering by distance
+    all_azimuths: list[np.ndarray] = []
+    all_cols: list[np.ndarray] = []
+    all_rows: list[np.ndarray] = []
+
+    for az, cols, rows, dists in zip(frame_azimuths, frame_model_cols, frame_model_rows, frame_distances):
+        far_mask = dists > min_range_m
+        all_azimuths.append(az[far_mask].astype(np.float64))
+        all_cols.append(cols[far_mask].astype(np.int64))
+        all_rows.append(rows[far_mask].astype(np.int64))
+
+    if not all_azimuths:
+        return model_params
+
+    cat_azimuths = np.concatenate(all_azimuths)
+    cat_cols = np.concatenate(all_cols)
+    cat_rows = np.concatenate(all_rows)
+
+    if len(cat_azimuths) == 0:
+        return model_params
+
+    for _ in range(n_iterations):
+        # Compute residual = actual_az - (column_azimuths[col] + row_offsets[row])
+        predicted = column_azimuths[cat_cols] + row_offsets[cat_rows]
+        residual = cat_azimuths - predicted
+        # Wrap residual to [-pi, pi]
+        residual = np.arctan2(np.sin(residual), np.cos(residual))
+
+        # Per-column correction: median of residuals grouped by column (vectorized)
+        col_correction = _grouped_median(residual, cat_cols, n_columns, min_count=3)
+        column_azimuths += col_correction
+
+        # Recompute residual after column correction
+        predicted = column_azimuths[cat_cols] + row_offsets[cat_rows]
+        residual = cat_azimuths - predicted
+        residual = np.arctan2(np.sin(residual), np.cos(residual))
+
+        # Per-row correction: median of residuals grouped by row (vectorized)
+        row_correction = _grouped_median(residual, cat_rows, n_rows, min_count=3)
+        row_offsets += row_correction
+
+    # Monotonicity enforcement on column_azimuths (wrap fix for CW spinning).
+    # Unwrap then re-normalize so azimuths are strictly decreasing.
+    column_azimuths_unwrapped = np.unwrap(column_azimuths)
+    # Normalize to (-pi, pi]
+    column_azimuths_norm = ((column_azimuths_unwrapped + np.pi) % (2 * np.pi)) - np.pi
+    # Force strictly decreasing for CW: values above first element get wrapped down
+    column_azimuths_norm[column_azimuths_norm > column_azimuths_norm[0]] -= 2 * np.pi
+
+    return RowOffsetStructuredSpinningLidarModelParameters(
+        spinning_frequency_hz=model_params.spinning_frequency_hz,
+        spinning_direction=model_params.spinning_direction,
+        n_rows=n_rows,
+        n_columns=n_columns,
+        row_elevations_rad=model_params.row_elevations_rad,
+        column_azimuths_rad=column_azimuths_norm.astype(np.float32),
+        row_azimuth_offsets_rad=row_offsets.astype(np.float32),
+    )
+
+
+def compute_model_consistency(
+    directions: np.ndarray,
+    model_element: np.ndarray,
+    distances: np.ndarray,
+    model_params: RowOffsetStructuredSpinningLidarModelParameters,
+    far_range_m: float = 20.0,
+) -> tuple[float, float, float]:
+    """Compute model consistency metrics.
+
+    Compares stored direction vectors against model-predicted directions to
+    quantify alignment quality.
+
+    Args:
+        directions: Stored direction unit vectors [N, 3] float32.
+        model_element: Model element indices [N, 2] uint16 as (row, col).
+        distances: Per-point distances [N] float32.
+        model_params: Lidar model parameters.
+        far_range_m: Distance threshold for far-range metric.
+
+    Returns:
+        (mean_err_all_deg, mean_err_far_deg, mean_az_shift_deg):
+            - mean_err_all_deg: Mean angular error across all valid points (degrees).
+            - mean_err_far_deg: Mean angular error for far-range points (degrees).
+            - mean_az_shift_deg: Mean systematic azimuth shift for far-range points (degrees).
+    """
+    model_row = model_element[:, 0].astype(np.int64)
+    model_col = model_element[:, 1].astype(np.int64)
+
+    # Predicted azimuth and elevation from model
+    model_az = model_params.column_azimuths_rad[model_col].astype(np.float64) + model_params.row_azimuth_offsets_rad[
+        model_row
+    ].astype(np.float64)
+    model_el = model_params.row_elevations_rad[model_row].astype(np.float64)
+
+    # Predicted direction vectors
+    cos_el = np.cos(model_el)
+    model_dir = np.stack([cos_el * np.cos(model_az), cos_el * np.sin(model_az), np.sin(model_el)], axis=1).astype(
+        np.float32
+    )
+
+    # Only compute for points with nonzero distance (valid returns)
+    nonzero_mask = distances > 0
+    if not nonzero_mask.any():
+        return (0.0, 0.0, 0.0)
+
+    cos_angle = np.clip(np.sum(directions[nonzero_mask] * model_dir[nonzero_mask], axis=1), -1.0, 1.0)
+    ang_err_deg = np.degrees(np.arccos(cos_angle))
+
+    mean_err_all_deg = float(ang_err_deg.mean())
+
+    # Far-range metrics
+    far_mask = distances[nonzero_mask] > far_range_m
+    if far_mask.any():
+        mean_err_far_deg = float(ang_err_deg[far_mask].mean())
+
+        # Systematic azimuth shift (signed)
+        actual_az = np.arctan2(
+            directions[nonzero_mask][far_mask, 1],
+            directions[nonzero_mask][far_mask, 0],
+        )
+        model_az_far = model_az[nonzero_mask][far_mask]
+        az_shift = np.arctan2(np.sin(actual_az - model_az_far), np.cos(actual_az - model_az_far))
+        mean_az_shift_deg = float(np.degrees(az_shift.mean()))
+    else:
+        mean_err_far_deg = mean_err_all_deg
+        mean_az_shift_deg = 0.0
+
+    return (mean_err_all_deg, mean_err_far_deg, mean_az_shift_deg)
diff --git a/tools/data_converter/nuscenes/main.py b/tools/data_converter/nuscenes/main.py
new file mode 100644
index 00000000..b2d4baae
--- /dev/null
+++ b/tools/data_converter/nuscenes/main.py
@@ -0,0 +1,23 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""nuScenes converter CLI entry point."""
+
+from tools.data_converter.cli import cli
+from tools.data_converter.nuscenes.converter import nuscenes  # noqa: F401 -- registers CLI command
+
+
+if __name__ == "__main__":
+    cli(show_default=True)
diff --git a/tools/data_converter/nuscenes/utils.py b/tools/data_converter/nuscenes/utils.py
new file mode 100644
index 00000000..ed282604
--- /dev/null
+++ b/tools/data_converter/nuscenes/utils.py
@@ -0,0 +1,618 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""nuScenes-specific utilities for the NCore V4 converter."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from functools import cache
+from typing import Any, Dict, List, Literal, Optional
+
+import numpy as np
+
+from nuscenes.nuscenes import NuScenes
+from nuscenes.utils.data_classes import Box
+from pyquaternion import Quaternion
+
+from ncore.impl.data.types import RowOffsetStructuredSpinningLidarModelParameters
+
+
+# --- Sensor ID mappings --------------------------------------------------------
+# Mapping from NCore sensor ID -> nuScenes channel name
+
+CAMERA_MAP: Dict[str, str] = {
+    "camera_front": "CAM_FRONT",
+    "camera_front_left": "CAM_FRONT_LEFT",
+    "camera_front_right": "CAM_FRONT_RIGHT",
+    "camera_back": "CAM_BACK",
+    "camera_back_left": "CAM_BACK_LEFT",
+    "camera_back_right": "CAM_BACK_RIGHT",
+}
+
+LIDAR_ID = "lidar_top"
+LIDAR_CHANNEL = "LIDAR_TOP"
+
+RADAR_MAP: Dict[str, str] = {
+    "radar_front": "RADAR_FRONT",
+    "radar_front_left": "RADAR_FRONT_LEFT",
+    "radar_front_right": "RADAR_FRONT_RIGHT",
+    "radar_back_left": "RADAR_BACK_LEFT",
+    "radar_back_right": "RADAR_BACK_RIGHT",
+}
+
+# nuScenes category name -> NCore class_id mapping
+NUSCENES_CATEGORY_MAP: Dict[str, str] = {
+    "vehicle.car": "car",
+    "vehicle.truck": "truck",
+    "vehicle.bus.bendy": "bus",
+    "vehicle.bus.rigid": "bus",
+    "vehicle.construction": "construction_vehicle",
+    "vehicle.motorcycle": "motorcycle",
+    "vehicle.bicycle": "bicycle",
+    "vehicle.trailer": "trailer",
+    "vehicle.emergency.ambulance": "emergency_vehicle",
+    "vehicle.emergency.police": "emergency_vehicle",
+    "human.pedestrian.adult": "pedestrian",
+    "human.pedestrian.child": "pedestrian",
+    "human.pedestrian.construction_worker": "pedestrian",
+    "human.pedestrian.police_officer": "pedestrian",
+    "movable_object.barrier": "barrier",
+    "movable_object.trafficcone": "traffic_cone",
+}
+
+# HDL-32E constants
+HDL32E_N_BEAMS: int = 32
+HDL32E_N_TARGET_COLS: int = 1085  # nominal: 50000us / 46.08us per column cycle = 1085.07
+HDL32E_SCAN_DURATION_US: int = 50_000  # fallback for single-frame edge cases
+
+
+# --- nuScenes DB helpers -------------------------------------------------------
+
+
+@cache
+def get_nuscenes(version: str, dataroot: str) -> NuScenes:
+    """Cached nuScenes DB loader to avoid reloading for multiple scenes."""
+    return NuScenes(version=version, dataroot=dataroot, verbose=False)
+
+
+def get_sweep_tokens(nusc: NuScenes, scene_record: Dict[str, Any], channel: str) -> List[str]:
+    """Return ordered list of sample_data tokens for all sweeps in a scene for a given channel.
+
+    This includes both keyframe and non-keyframe (interleaved) sweeps.
+    """
+    result: List[str] = []
+    sample_token = scene_record["first_sample_token"]
+    sample_record = nusc.get("sample", sample_token)
+    sweep_token = sample_record["data"][channel]
+
+    while sweep_token:
+        result.append(sweep_token)
+        sample_data_record = nusc.get("sample_data", sweep_token)
+        sweep_token = sample_data_record["next"]
+
+    return result
+
+
+def get_sample_records(nusc: NuScenes, scene_record: Dict[str, Any]) -> List[Dict[str, Any]]:
+    """Return ordered list of keyframe sample records for a scene."""
+    result: List[Dict[str, Any]] = []
+    sample_token = scene_record["first_sample_token"]
+
+    while sample_token:
+        sample_record = nusc.get("sample", sample_token)
+        result.append(sample_record)
+        sample_token = sample_record["next"]
+
+    return result
+
+
+def resolve_scene_token(nusc: NuScenes, scene_token: Optional[str], scene_name: Optional[str]) -> Optional[str]:
+    """Resolve a scene identifier to its token.
+
+    If scene_token is provided, validate it exists and return it.
+    If scene_name is provided, look it up and return its token.
+    If neither provided, return None (meaning: convert all scenes).
+    """
+    if scene_token is not None and scene_name is not None:
+        raise ValueError("Specify at most one of --scene-token or --scene-name, not both.")
+
+    if scene_token is not None:
+        all_tokens = {s["token"] for s in nusc.scene}
+        if scene_token not in all_tokens:
+            raise ValueError(
+                f"Scene token '{scene_token}' not found in dataset. Available: {sorted(all_tokens)[:5]}..."
+            )
+        return scene_token
+
+    if scene_name is not None:
+        name_to_token = {s["name"]: s["token"] for s in nusc.scene}
+        if scene_name not in name_to_token:
+            raise ValueError(f"Scene name '{scene_name}' not found. Available: {sorted(name_to_token.keys())[:5]}...")
+        return name_to_token[scene_name]
+
+    return None
+
+
+# --- Structured lidar model utilities ------------------------------------------
+
+
+@dataclass
+class LidarColumnAlignment:
+    """Result of column alignment between a lidar spin and a static model."""
+
+    spin_column_range: range  # columns to use from the current frame
+    static_column_range: range  # corresponding columns in the model
+    mean_alignment_error_rad: float
+
+
+def compute_lidar_column_alignment(
+    spin_azimuths_rad: np.ndarray,
+    model_column_azimuths_rad: np.ndarray,
+    max_column_shift: int = 20,
+) -> LidarColumnAlignment:
+    """Align measured column azimuths to a static model via brute-force shift search.
+
+    Tries integer column shifts in [-max_column_shift, +max_column_shift] and selects
+    the shift minimizing the mean angular difference. Uses arccos(cos(...)) for
+    wrap-around safety.
+
+    This is a generic utility usable for any spinning lidar where column count
+    varies between frames.
+
+    Args:
+        spin_azimuths_rad: Per-column measured azimuths for the current frame [n_spin_cols].
+        model_column_azimuths_rad: Static model column azimuths [n_model_cols].
+        max_column_shift: Maximum integer shift to search (positive and negative).
+
+    Returns:
+        LidarColumnAlignment with optimal spin/static column ranges and error metrics.
+    """
+    best_spin_range = range(0, 0)
+    best_static_range = range(0, 0)
+    best_mean_error: Optional[float] = None
+
+    n_spin = len(spin_azimuths_rad)
+    n_model = len(model_column_azimuths_rad)
+
+    for shift in range(-max_column_shift, max_column_shift + 1):
+        spin_start = max(-shift, 0)
+        static_start = max(shift, 0)
+        spin_stop = min(n_spin, n_model - static_start + spin_start)
+        static_stop = static_start + (spin_stop - spin_start)
+
+        if spin_stop <= spin_start:
+            continue
+
+        # cos-based angular difference (handles wrap-around)
+        abs_error_rad = np.arccos(
+            np.clip(
+                np.cos(spin_azimuths_rad[spin_start:spin_stop] - model_column_azimuths_rad[static_start:static_stop]),
+                -1.0,
+                1.0,
+            )
+        )
+
+        mean_err = float(abs_error_rad.mean())
+
+        if best_mean_error is None or mean_err < best_mean_error:
+            best_mean_error = mean_err
+            best_spin_range = range(spin_start, spin_stop)
+            best_static_range = range(static_start, static_stop)
+
+    assert best_mean_error is not None
+
+    return LidarColumnAlignment(
+        spin_column_range=best_spin_range,
+        static_column_range=best_static_range,
+        mean_alignment_error_rad=best_mean_error,
+    )
+
+
+def derive_lidar_model_from_frame(
+    xyz_mc: np.ndarray,
+    ring_index: np.ndarray,
+    n_beams: int,
+    n_target_cols: int,
+    spinning_direction: Literal["cw", "ccw"],
+    spinning_frequency_hz: float,
+    min_range_m: float = 20.0,
+    fallback_min_range_m: float = 5.0,
+    xyz_decompensated: Optional[np.ndarray] = None,
+) -> Optional[RowOffsetStructuredSpinningLidarModelParameters]:
+    """Derive structured lidar model parameters from a single frame.
+
+    If xyz_decompensated is provided, azimuths and elevations are extracted from the
+    decompensated (raw) point cloud, which gives ~4x better accuracy than using MC'd
+    data directly (0.08 deg vs 0.33 deg per-column noise). The MC'd data is still used
+    for range filtering (distance computation).
+
+    Args:
+        xyz_mc: Motion-compensated point cloud [N, 3] float32.
+        ring_index: Beam ID per point [N], values 0..n_beams-1.
+        n_beams: Number of beams (32 for HDL-32E).
+        n_target_cols: Expected number of columns. Frame is rejected if column count differs.
+        spinning_direction: "cw" or "ccw".
+        spinning_frequency_hz: Nominal spinning frequency.
+        min_range_m: Minimum range for "good" points (default 20m).
+        fallback_min_range_m: If <50% columns covered at min_range_m, retry at this range.
+        xyz_decompensated: If provided, decompensated point cloud [N, 3] used for
+            azimuth/elevation extraction (much more accurate than MC'd data).
+
+    Returns:
+        Model parameters, or None if frame doesn't meet quality criteria.
+    """
+    n_points = len(xyz_mc)
+    n_cols = n_points // n_beams
+    if n_cols != n_target_cols:
+        return None
+
+    # Use decompensated data for azimuth/elevation if available (4x more accurate);
+    # fall back to MC'd data. Distance filtering always uses MC'd data (range is preserved).
+    xyz_for_angles = xyz_decompensated if xyz_decompensated is not None else xyz_mc
+
+    col_idx = np.arange(n_points) // n_beams
+    dist = np.linalg.norm(xyz_mc, axis=1)
+    azimuth = np.arctan2(xyz_for_angles[:, 1], xyz_for_angles[:, 0])
+    elevation_xy_range = np.sqrt(xyz_for_angles[:, 0] ** 2 + xyz_for_angles[:, 1] ** 2)
+    elevation = np.arctan2(xyz_for_angles[:, 2], elevation_xy_range)
+
+    # Try to extract per-column azimuths at min_range_m; fall back if insufficient coverage
+    effective_min_range = min_range_m
+    for attempt_range in [min_range_m, fallback_min_range_m]:
+        col_az = np.full(n_cols, np.nan, dtype=np.float64)
+        for c in range(n_cols):
+            mask = (col_idx == c) & (dist > attempt_range)
+            if mask.sum() >= 3:
+                col_az[c] = np.median(azimuth[mask])
+
+        coverage = np.sum(~np.isnan(col_az)) / n_cols
+        if coverage >= 0.5:
+            effective_min_range = attempt_range
+            break
+    else:
+        # Even fallback range doesn't give enough coverage
+        return None
+
+    # --- Column azimuths and per-row offsets ---
+    col_idx = np.arange(n_points) // n_beams
+    az_grid = azimuth.reshape(n_cols, n_beams)
+    dist_grid = dist.reshape(n_cols, n_beams)
+
+    # Pick reference ring with best far-range coverage
+    ring_coverage = np.array([np.sum(dist_grid[:, r] > effective_min_range) for r in range(n_beams)])
+    ref_ring = int(np.argmax(ring_coverage))
+
+    # Determine starting azimuth from far-range data at mid-scan (robust to boundary noise)
+    # Determine the reference ring's azimuth intercept via least-squares fit with
+    # fixed step (-2*pi/n_cols). Using a single mid-scan anchor point is biased by
+    # MC distortion; fitting across all far-range columns averages it out.
+    col_az_ref = np.full(n_cols, np.nan, dtype=np.float64)
+    for c in range(n_cols):
+        if dist_grid[c, ref_ring] > effective_min_range:
+            col_az_ref[c] = az_grid[c, ref_ring]
+
+    valid_ref = ~np.isnan(col_az_ref)
+    if valid_ref.sum() < 10:
+        return None
+
+    valid_cols = np.where(valid_ref)[0].astype(np.float64)
+    valid_az_unwrapped = np.unwrap(col_az_ref[valid_ref])
+
+    step_rad = -2.0 * np.pi / n_cols
+    # Fit intercept only (step is fixed from physics): intercept = mean(az - step * col)
+    intercept = float(np.mean(valid_az_unwrapped - step_rad * valid_cols))
+
+    # Column azimuths: exactly one full rotation (2*pi) over n_cols columns (CW).
+    col_indices = np.arange(n_cols, dtype=np.float64)
+    column_azimuths_f64 = intercept + step_rad * col_indices
+    # Normalize to (-pi, pi]
+    column_azimuths_f64 = ((column_azimuths_f64 + np.pi) % (2 * np.pi)) - np.pi
+    column_azimuths_rad = column_azimuths_f64.astype(np.float32)
+    # Force strictly decreasing through the wrap-around point
+    column_azimuths_rad[column_azimuths_rad > column_azimuths_rad[0]] -= np.float32(2 * np.pi)
+
+    # --- Per-row azimuth offsets ---
+    # The HDL-32E fires beams sequentially within each column, causing each beam to
+    # fire at a slightly different azimuth (up to ~0.7 deg for edge beams).
+    # We compute offsets from far-range points (>20m preferred) across all columns.
+    # Far-range is preferred because MC translational distortion has minimal angular
+    # effect at long range, giving more accurate offset estimates.
+    offset_min_range = max(min_range_m, 20.0)
+    row_azimuth_offsets = np.zeros(n_beams, dtype=np.float32)
+
+    for r in range(n_beams):
+        diffs = []
+        for c in range(n_cols):
+            if dist_grid[c, r] > offset_min_range:
+                measured = az_grid[c, r]
+                base = column_azimuths_rad[c]
+                diff = np.arctan2(np.sin(measured - base), np.cos(measured - base))
+                diffs.append(diff)
+        # Fall back to effective_min_range if not enough far-range points
+        if len(diffs) < 10:
+            diffs = []
+            for c in range(n_cols):
+                if dist_grid[c, r] > effective_min_range:
+                    measured = az_grid[c, r]
+                    base = column_azimuths_rad[c]
+                    diff = np.arctan2(np.sin(measured - base), np.cos(measured - base))
+                    diffs.append(diff)
+        if len(diffs) >= 10:
+            row_azimuth_offsets[r] = np.median(diffs)
+
+    # Subtract reference ring's offset (column_azimuths represents that ring)
+    row_azimuth_offsets -= row_azimuth_offsets[ref_ring]
+    # Reverse to match model row order (row 0 = ring n_beams-1 = highest elevation)
+    row_azimuth_offsets = row_azimuth_offsets[::-1]
+
+    # --- Per-ring elevation ---
+    row_elevations = np.zeros(n_beams, dtype=np.float32)
+    for r in range(n_beams):
+        ring_mask = (ring_index == r) & (dist > effective_min_range)
+        if ring_mask.any():
+            row_elevations[r] = np.median(elevation[ring_mask])
+        else:
+            ring_mask_all = ring_index == r
+            if ring_mask_all.any():
+                row_elevations[r] = np.median(elevation[ring_mask_all])
+
+    # Reverse elevation order: model expects row 0 = highest elevation (ring n_beams-1)
+    row_elevations = row_elevations[::-1]
+
+    return RowOffsetStructuredSpinningLidarModelParameters(
+        spinning_frequency_hz=spinning_frequency_hz,
+        spinning_direction=spinning_direction,
+        n_rows=n_beams,
+        n_columns=n_cols,
+        row_elevations_rad=row_elevations,
+        column_azimuths_rad=column_azimuths_rad,
+        row_azimuth_offsets_rad=row_azimuth_offsets,
+    )
+
+
+def derive_lidar_model_simple(
+    xyz_decompensated: np.ndarray,
+    n_beams_per_column: int,
+    n_target_cols: int,
+    spinning_direction: Literal["cw", "ccw"],
+    spinning_frequency_hz: float,
+    min_valid_distance_m: float = 0.5,
+) -> Optional[RowOffsetStructuredSpinningLidarModelParameters]:
+    """Derive structured lidar model directly from decompensated point cloud.
+
+    Uses the same approach as extracting parameters from raw sensor data:
+    - column_azimuths from a reference row's per-column azimuths
+    - row_azimuth_offsets from the center column (each row vs reference row)
+    - row_elevations from the center column
+
+    Points below min_valid_distance_m are treated as "no return" sentinel values
+    (the HDL-32E stores ~0.1m vectors for missed returns) and excluded.
+
+    Args:
+        xyz_decompensated: Decompensated (raw) point cloud [N, 3].
+        n_beams_per_column: Beams per column (32 for HDL-32E).
+        n_target_cols: Expected column count. Returns None if different.
+        spinning_direction: "cw" or "ccw".
+        spinning_frequency_hz: Spinning frequency in Hz.
+        min_valid_distance_m: Minimum distance to consider a point as a valid return.
+
+    Returns:
+        Model parameters, or None if frame doesn't match target column count.
+    """
+    n_points = len(xyz_decompensated)
+    n_cols = n_points // n_beams_per_column
+    if n_cols != n_target_cols:
+        return None
+
+    # Reshape into [n_cols, n_beams_per_column, 3] grid
+    xyz_grid = xyz_decompensated.reshape(n_cols, n_beams_per_column, 3)
+
+    # Compute per-cell angles and distances from the grid
+    dist_grid = np.linalg.norm(xyz_grid, axis=2)
+    az_grid = np.arctan2(xyz_grid[:, :, 1], xyz_grid[:, :, 0])
+    xy_range_grid = np.sqrt(xyz_grid[:, :, 0] ** 2 + xyz_grid[:, :, 1] ** 2)
+    el_grid = np.arctan2(xyz_grid[:, :, 2], xy_range_grid)
+
+    # Valid mask: exclude "no return" sentinel points (stored as ~0.1m vectors)
+    valid_grid = dist_grid > min_valid_distance_m
+
+    # Reference row: the one with the most valid returns across all columns
+    row_valid_count = valid_grid.sum(axis=0)
+    ref_row = int(np.argmax(row_valid_count))
+
+    # column_azimuths: reference row's per-column azimuths (monotonic after decompensation).
+    # For columns where the reference row has a sentinel, interpolate from valid neighbors.
+    ref_valid = valid_grid[:, ref_row]
+    if ref_valid.sum() < n_cols * 0.9:
+        # Not enough valid data in the reference row -- frame is unsuitable
+        return None
+
+    col_az = az_grid[:, ref_row].astype(np.float64)
+    if not ref_valid.all():
+        valid_indices = np.where(ref_valid)[0]
+        valid_az_unwrapped = np.unwrap(col_az[ref_valid])
+        col_az = np.interp(np.arange(n_cols, dtype=np.float64), valid_indices, valid_az_unwrapped)
+
+    column_azimuths_rad = col_az.astype(np.float32)
+    # Normalize wrap-around: force strictly decreasing for CW
+    column_azimuths_rad[column_azimuths_rad > column_azimuths_rad[0]] -= np.float32(2 * np.pi)
+
+    # row_azimuth_offsets: each row's azimuth offset relative to reference row.
+    # Start with a small window around center column; progressively widen if a ring
+    # has no valid returns there (lower beams often only produce sentinels near center).
+    center_col = n_cols // 2
+    row_azimuth_offsets = np.zeros(n_beams_per_column, dtype=np.float32)
+    for r in range(n_beams_per_column):
+        valid_diffs = []
+        for half_width in [5, 50, n_cols // 2]:
+            window_start = max(0, center_col - half_width)
+            window_stop = min(n_cols, center_col + half_width + 1)
+            for c in range(window_start, window_stop):
+                if valid_grid[c, r]:
+                    base = column_azimuths_rad[c]
+                    diff = np.arctan2(np.sin(az_grid[c, r] - base), np.cos(az_grid[c, r] - base))
+                    valid_diffs.append(diff)
+            if valid_diffs:
+                break
+        if valid_diffs:
+            row_azimuth_offsets[r] = np.median(valid_diffs)
+
+    # row_elevations: median across all valid columns per ring (robust to occasional sentinels)
+    row_elevations = np.zeros(n_beams_per_column, dtype=np.float32)
+    for r in range(n_beams_per_column):
+        valid_cols_r = valid_grid[:, r]
+        if valid_cols_r.any():
+            row_elevations[r] = np.median(el_grid[valid_cols_r, r])
+
+    # Reverse row order: model expects row 0 = highest elevation (ring n_beams-1)
+    row_azimuth_offsets = row_azimuth_offsets[::-1]
+    row_elevations = row_elevations[::-1]
+
+    return RowOffsetStructuredSpinningLidarModelParameters(
+        spinning_frequency_hz=spinning_frequency_hz,
+        spinning_direction=spinning_direction,
+        n_rows=n_beams_per_column,
+        n_columns=n_cols,
+        row_elevations_rad=row_elevations,
+        column_azimuths_rad=column_azimuths_rad,
+        row_azimuth_offsets_rad=row_azimuth_offsets,
+    )
+
+
+def extract_column_azimuths(
+    xyz: np.ndarray,
+    col_idx: np.ndarray,
+    n_cols: int,
+    min_range_m: float = 20.0,
+    min_points_per_col: int = 3,
+) -> np.ndarray:
+    """Extract per-column median azimuths from far-range points.
+
+    Args:
+        xyz: Point cloud [N, 3].
+        col_idx: Column index per point [N].
+        n_cols: Total number of columns.
+        min_range_m: Minimum point range to include.
+        min_points_per_col: Minimum points needed for a valid column azimuth.
+
+    Returns:
+        Per-column azimuths [n_cols], with NaN for columns without enough data.
+    """
+    dist = np.linalg.norm(xyz, axis=1)
+    azimuth = np.arctan2(xyz[:, 1], xyz[:, 0])
+    col_az = np.full(n_cols, np.nan, dtype=np.float64)
+
+    for c in range(n_cols):
+        mask = (col_idx == c) & (dist > min_range_m)
+        if mask.sum() >= min_points_per_col:
+            col_az[c] = np.median(azimuth[mask])
+
+    return col_az
+
+
+# --- Cuboid / annotation helpers -----------------------------------------------
+
+
+def get_boxes_for_sample_data(nusc: NuScenes, sample_data_token: str) -> List[Box]:
+    """Get annotation boxes for a sample_data record.
+
+    If the sample_data is a keyframe, returns the annotations for that sample directly.
+    If it is an intermediate sweep, interpolates box positions linearly between
+    the bracketing keyframes.
+
+    Each returned Box has:
+    - .center: [x, y, z] in the global frame
+    - .wlh: [width, length, height]
+    - .orientation: Quaternion
+    - .velocity: [vx, vy, vz] (may contain NaN)
+    - .name: category name
+    - .token: instance_token (used as track_id)
+
+    Returns:
+        List of Box objects in the global (world) coordinate frame.
+    """
+    sd_record = nusc.get("sample_data", sample_data_token)
+    curr_sample_record = nusc.get("sample", sd_record["sample_token"])
+
+    if curr_sample_record["prev"] == "" or sd_record["is_key_frame"]:
+        # Keyframe or first sample: return annotations directly
+        boxes = []
+        for ann_token in curr_sample_record["anns"]:
+            record = nusc.get("sample_annotation", ann_token)
+            velocity = nusc.box_velocity(record["token"])
+            box = Box(
+                record["translation"],
+                record["size"],
+                Quaternion(record["rotation"]),
+                velocity=tuple(velocity),
+                name=record["category_name"],
+                token=record["instance_token"],
+            )
+            boxes.append(box)
+        return boxes
+
+    # Non-keyframe: interpolate between previous and current keyframe annotations
+    prev_sample_record = nusc.get("sample", curr_sample_record["prev"])
+
+    curr_ann_recs = [nusc.get("sample_annotation", token) for token in curr_sample_record["anns"]]
+    prev_ann_recs = [nusc.get("sample_annotation", token) for token in prev_sample_record["anns"]]
+
+    # Map instance tokens to previous annotation records
+    prev_inst_map = {entry["instance_token"]: entry for entry in prev_ann_recs}
+
+    t0 = prev_sample_record["timestamp"]
+    t1 = curr_sample_record["timestamp"]
+    t = sd_record["timestamp"]
+
+    # Clamp t to [t0, t1] for safety
+    t = max(t0, min(t1, t))
+
+    boxes: List[Box] = []
+    for curr_ann_rec in curr_ann_recs:
+        instance_token = curr_ann_rec["instance_token"]
+
+        if instance_token in prev_inst_map:
+            prev_ann_rec = prev_inst_map[instance_token]
+
+            # Interpolate center
+            center = [
+                np.interp(t, [t0, t1], [c0, c1])
+                for c0, c1 in zip(prev_ann_rec["translation"], curr_ann_rec["translation"])
+            ]
+
+            # Interpolate orientation via SLERP
+            rotation = Quaternion.slerp(
+                q0=Quaternion(prev_ann_rec["rotation"]),
+                q1=Quaternion(curr_ann_rec["rotation"]),
+                amount=(t - t0) / (t1 - t0),
+            )
+        else:
+            # New instance -- use current annotation directly
+            center = curr_ann_rec["translation"]
+            rotation = Quaternion(curr_ann_rec["rotation"])
+
+        velocity = nusc.box_velocity(curr_ann_rec["token"])
+        box = Box(
+            center,
+            curr_ann_rec["size"],
+            rotation,
+            velocity=tuple(velocity),
+            name=curr_ann_rec["category_name"],
+            token=instance_token,
+        )
+        boxes.append(box)
+
+    return boxes