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name: inference-and-custom-data description: "Run and adapt OpenPCDet demo inference, custom point-cloud inputs, visualization, and CustomDataset workflows." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

OpenPCDet Inference and Custom Data

Use this sub-skill for demo inference, .bin/.npy point clouds, checkpoint/config pairing, visualization, non-visual prediction adaptation, and custom dataset setup.

Fast route

1. Verify runtime/native ops with ../../scripts/inspect_openpcdet_runtime.py --require-cuda-ops.
2. Validate point-cloud files with scripts/check_point_cloud_array.py.
3. Read references/demo-and-custom-data.md for DemoDataset and CustomDataset semantics.
4. Build a demo command with ../../scripts/plan_openpcdet_command.py --mode demo.
5. Route full dataset info/database generation to ../data-preparation/SKILL.md.

Demo command shape

# from the generated skill root
python scripts/plan_openpcdet_command.py --repo <checkout> --mode demo --cfg <config.yaml> --ckpt <checkpoint.pth> --data-path <point-file-or-dir> --ext .bin

The helper prints the command by default. Add --execute only after config/checkpoint/data/visualization dependencies are confirmed.

Critical caveats

• .bin demo files are read as float32 and reshaped to N x 4.
• .npy demo files must be 2-D arrays with the expected point feature columns.
• The config's POINT_FEATURE_ENCODING, point cloud range, class names, and checkpoint must match.
• Open3D or Mayavi is needed for interactive drawing; non-visual inference should adapt the demo loop to save predictions instead.