Agent skill · nvidia
foundationpose-pipeline
Adapt BOP datasets, run the FoundationPose perception pipeline with TAO depth, and evaluate or re-score pose results. Use for dataset runs and result comparisons; environment installation belongs to foundationpose-setup.
What it needs
About 5k tokens when loaded.
What this skill does
Run and evaluate the FoundationPose perception pipeline Purpose Run depth, SAM3 segmentation, and FoundationPose on BOP-format datasets using the TAO Deploy TensorRT depth engine. Adapt datasets, preserve run provenance, and interpret pose metrics. For missing dependencies or engine construction, use foundationpose-setup if installed, or the product checkout's README Install and Verify sections. Requirements Locate the user's product checkout by pyproject.toml (project foundationpose-perception-pipeline), script/runpipeline.py, and config/defaults.yaml. Run commands from that root, not from this installed skill's directory. A catalog install supplies instructions, not the product code, datasets, or weights. If execution was requested and no checkout exists, obtain it from the URL above and complete setup first. For advice or analysis of supplied artifacts, use those inputs without cloning or loading models. Execution requires the product's Python 3.12 venv, authorized SAM3 checkpoint access, the built FoundationPose library, an adapted dataset, a matching TAO engine with its sidecar, and sufficient GPU memory. Read the checkout's README Configuration and Dataset adaptation sections for profile paths; read ARCHITECTURE.md Outputs for the matching artifact schema. Set absolute paths before GPU work: Do not mix libraries from another venv into this path. Skipping the check can cause pose to fail after depth has already completed. Instructions 1. Resolve the task and inputs Identify the profile, dataset name, source or adapted scene paths, engine, ground-truth availability, and output directory. <profile> and <dataset> may differ. --config selects a profile; it does not replace a required --dataset. Same-named profiles can be inferred by commands that take --dataset. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills nvidia/foundationpose-pipeline