Individual skill
tao-train-pointpillars
PointPillars for 3D object detection from LiDAR point clouds. Encodes point clouds into a pseudo-image via a pillar-based representation, then applies 2D detection — used in autonomous driving and robotics. Use when training, evaluating, exporting, pruning, retraining, or running inference for a TAO PointPillars model. Trigger phrases include "train PointPillars", "LiDAR 3D detection", "point-cloud object detection", "pillar-based 3D detector".
skills/tao-train-pointpillars/SKILL.md
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- Last checked
- Nov 9, 2026
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- Repository last updated
- Nov 9, 2026
- License
- Apache-2.0
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- point Declared skill tag
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- 3d Declared skill tag
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- engineering Derived category
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# PointPillars
> **Standalone install?** If this session was not initialized by the TAO skill bank plugin, run the `tao-setup` skill first (host preflight, credentials, cross-skill discovery).
PointPillars for 3D object detection from LiDAR point clouds. Encodes point clouds into a pseudo-image via pillar-based representation, then applies 2D detection. Used in autonomous driving / robotics.
Typically trained from scratch. Provide train.resume_training_checkpoint_path to resume.
For TAO Deploy TensorRT actions (`gen_trt_engine`, TensorRT `evaluate`, and TensorRT `inference`), read `references/tao-deploy-pointpillars.md` first. Deploy spec templates live in this skill's `references/` folder with the `spec_template_deploy_*.yaml` prefix.
The packaged PyTorch PointPillars CLI supports `dataset_convert`, `train`, `evaluate`, `inference`, `export`, and `prune`. It does not expose a parent-model `gen_trt_engine` action; TensorRT engine generation is deploy-only. It also does not expose a separate `retrain` subcommand. Retraining from a pruned model uses `pointpillars train -e ...` with `train.pruned_model_path` populated.
## Dataclass Schemas
