vectorspacelab/arex-skill/openpcdet

OpenPCDet Operating Skill

Use this skill when a task involves OpenPCDet, the pcdet Python package, OpenPCDet YAML configs, 3D point-cloud detection datasets, or OpenPCDet training/evaluation/demo workflows.

Route first

• Need installation, CUDA extension, spconv, import, or environment diagnosis? Read sub-skills/runtime-and-ops/SKILL.md.
• Need dataset layout, info pickle, ground-truth database, split, or DATA_CONFIG help? Read sub-skills/data-preparation/SKILL.md.
• Need to train, resume, evaluate, run distributed jobs, or interpret outputs/checkpoints? Read sub-skills/training-and-evaluation/SKILL.md.
• Need demo inference, custom point-cloud files, CustomDataset, visualization, or non-visual inference adaptation? Read sub-skills/inference-and-custom-data/SKILL.md.
• Need config/model registry, YAML overrides, detector family selection, BEVFusion/MPPNet/CaDDN/VoxelNeXt/DSVT notes, or new model extension points? Read sub-skills/models-and-configs/SKILL.md.

Minimal public install and verification shape

For a fresh OpenPCDet checkout and a CUDA-capable Python environment, the package build shape is:

python -m pip install --no-build-isolation -e <OpenPCDet-checkout>
python scripts/inspect_openpcdet_runtime.py --repo <OpenPCDet-checkout> --require-cuda-ops

The build command is public and reproducible, but the exact CUDA/PyTorch/spconv variants must match the user's environment; read sub-skills/runtime-and-ops/SKILL.md before repairing a failing build.

Use bundled helpers instead of memorizing repository scripts

The skill bundles safe helper scripts under scripts/ and sub-skill scripts/. They do not run expensive dataset/training jobs unless explicitly asked.

• scripts/inspect_openpcdet_runtime.py checks imports, PyTorch CUDA, spconv/cumm, optional packages, and compiled OpenPCDet ops.
• scripts/summarize_openpcdet_config.py loads an OpenPCDet YAML config and summarizes dataset/model/optimization fields without building data loaders or models.
• scripts/plan_openpcdet_command.py constructs train/test/demo/dataset-prep commands for an OpenPCDet checkout and only executes when --execute is supplied.
• sub-skills/data-preparation/scripts/check_openpcdet_dataset_layout.py checks expected dataset folders and generated info/database products.
• sub-skills/inference-and-custom-data/scripts/check_point_cloud_array.py validates .bin/.npy point-cloud shapes before demo/custom inference.
• sub-skills/models-and-configs/scripts/inventory_openpcdet_configs.py inventories config YAMLs and detector/dataset registry names.

Operating stance

1. Treat OpenPCDet as GPU-first. CPU-only imports do not prove model training, evaluation, or native op correctness.
2. Keep config, dataset root, checkpoint, and model family aligned. Most runtime failures come from mixing a config with the wrong data layout, class list, or checkpoint.
3. Prefer static/config/import probes before large jobs. Run full training/evaluation only after checking CUDA ops, spconv variant, dataset infos, ground-truth database products, and CLI command construction.
4. For future work in another checkout, use checkout-relative paths supplied by the user or the current task. Do not rely on the source checkout used to construct this skill.

Core references

• references/repo-provenance.md records source commit, package version, environment facts, and evidence paths.
• references/repo-routing-metadata.json contains router placement metadata for repo-skill import tooling.
• references/openpcdet-overview.md summarizes architecture, registries, and workflow entry points.
• references/troubleshooting.md covers cross-cutting install/import/data/config/job failures.
• references/source-script-map.md records which repository scripts were wrapped, adapted, or excluded.