Agent skill · NVIDIA
tao-train-pointpillars
PointPillars for 3D object detection from LiDAR point clouds. Encodes point clouds into a pseudo-image via a
What it needs
About 8k tokens when loaded.
What this skill does
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.resumetrainingcheckpointpath to resume. For TAO Deploy TensorRT actions (gentrtengine, TensorRT evaluate, and TensorRT inference), read references/tao-deploy-pointpillars.md first. Deploy spec templates live in this skill's references/ folder with the spectemplatedeploy.yaml prefix. The packaged PyTorch PointPillars CLI supports datasetconvert, train, evaluate, inference, export, and prune. It does not expose a parent-model gentrtengine 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.prunedmodelpath populated. Dataclass Schemas Generated TAO Core schemas are packaged in schemas/<action>.schema.json, with schemas/manifest.json listing available actions. Each generated schema also emits references/spectemplate<action>.yaml from the schema top-level default field. AutoML enablement is declared at the model layer in references/skillinfo.yaml via automlenabled. Runnable AutoML for an action requires schemas/<action>.schema.json and references/spectemplate<action>.yaml to exist and parse. Use the packaged selected-action schema for automldefaultparameters, automldisabledparameters, defaults, min/max bounds, enums, option weights, math conditions, dependencies, and popular parameters. Do not expect ~/tao-core at runtime; maintainers regenerate schemas/templates before packaging the skill bank. Train Action Policy This model is AutoML-enabled at the model layer. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills NVIDIA/tao-train-pointpillars