Foundationpose Pipeline
NVIDIAOfficialCreated by zwdoescodeDistributed by NVIDIA
- Skill
- Tool integration
- Software development
- Apache-2.0
- Medium risk
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill provides a structured workflow for running the FoundationPose perception pipeline. It covers the end-to-end process of adapting BOP datasets, executing inference with TAO-based depth engines, and performing detailed evaluations of pose estimation results. The skill emphasizes maintaining data provenance, validating engine configurations, and managing output directories to ensure reproducible comparisons between different model runs.
What it does
- Adapts BOP-format datasets for compatibility with the FoundationPose pipeline.
- Executes pose inference using TAO-deployed TensorRT depth engines.
- Performs automated evaluation and scoring of pose estimation results.
- Supports re-scoring of existing runs with updated parameters without re-running inference.
- Facilitates batch processing and comparative analysis across multiple datasets.
- Validates execution provenance through metadata and configuration inspection.
When to use it
- When you need to run pose estimation on new or existing BOP-format datasets.
- When comparing the accuracy of different pose estimation configurations.
- When you need to re-evaluate completed runs with different thresholds or cutoffs.
- When performing batch evaluations across multiple scenes or datasets.
- When verifying that depth engine configurations are correctly set up for inference.
Advantages
- Ensures consistent data adaptation and pipeline execution.
- Allows for efficient re-scoring without repeating computationally expensive inference.
- Provides structured output for comparing pose accuracy and depth metrics.
- Supports provenance tracking to ensure results are reproducible.
- Enables batch automation for large-scale dataset testing.
Usage examples
- “Adapt T-LESS and run one scene with the TAO depth engine.”
- The skill will trigger the dataset adaptation script for T-LESS and execute the pipeline on a single scene using the specified TAO engine.
- “Re-score this finished FoundationPose run at cutoff 4.5 and preserve the old report.”
- The skill will perform a re-evaluation of the existing run data using the new cutoff parameter and save the results to a separate directory.
- “Compare these pose summaries; did the 5 mm success rate improve at the same coverage?”
- The skill will analyze the provided pose summary files to compare vertex error rates and matched prediction counts between two runs.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/nvidia/foundationpose-pipeline/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip foundationpose-pipeline.zip -d .agents/skills
Documentation by zwdoescode
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/run_pipeline.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:
export FOUNDATIONPOSE_ROOT="$(realpath ../foundation-pose-inference-library)"
PIPELINE_SITE="$(realpath .venv/lib/python3.12/site-packages)"
export LD_LIBRARY_PATH="${PIPELINE_SITE}/tensorrt_libs:${PIPELINE_SITE}/nvidia/cu13/lib:${LD_LIBRARY_PATH:-}"
./.venv/bin/python -c "import ctypes; ctypes.CDLL('libcudart.so.13'); print('ok')"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.
| Request | Entry point |
|---|---|
| Convert a supported BOP dataset | tools/bop_adapt/adapt.py |
| Inference without pose ground truth | script/infer.py |
| Inference plus scoring | script/run_pipeline.py |
| Score a completed run with new scoring parameters | script/evaluate.py |
| Sweep several datasets | script/run_batch_eval.py |
A capture without scene_gt.json can use inference only. --no-depth-metrics skips collected
sensor-depth scoring; it does not remove the pose-ground-truth requirement for evaluation.
2. Adapt before building or checking an engine
Skip adaptation only for the pipeline's rig layout:
<split>/<scene>/rgb/<im_id>.png, one scene_camera.json per scene, and im_ids representing
rig cameras (base camera 0 in the shipped profiles).
./.venv/bin/python tools/bop_adapt/adapt.py --config <profile> --src <downloaded-dataset>The profile's dataset.name selects a registered adapter; --help exposes its flags. An unknown
adapter is not supported automatically. On static-scene datasets the adapter emits one scene
per usable (source scene, base frame) pair and reports skipped frames with no rectifiable partner.
Changing the baseline band changes the adapted data: rebuild GT caches and regenerate depth.
Engine building uses tools/build_tao_engine.py --shape-from-scene <adapted-scene>; see the
checkout's README Install section if the setup skill is unavailable. A raw BOP directory or raw
image dimensions do not establish the required rectified engine shape.
3. Validate inputs and prepare GT caches
./.venv/bin/python test/check_engine_depth_smoke.py \
--config <profile> --dataset <dataset> --engine <engine-path>Expect backend=tao, normalization=imagenet, a fixed shape, a plausible valid fraction, and no
cropping N rows warning. A stale sidecar, changed GPU/TensorRT/precision, changed max-width,
or cropping requires rebuilding the engine and regenerating depth. Do not bypass these checks.
For scoring runs with pose GT, precompute the cache:
./.venv/bin/python script/build_gt_cache.py --config <profile> --dataset <dataset>Use --config <profile> --all for all matching datasets. Missing collected depth calls for
--no-depth-metrics; missing scene_gt.json calls for inference only. Check the resolved
dataset.collected_depth_root using the actual path, not a shell command substitution.
4. Run only the work needed
For a new end-to-end run:
./.venv/bin/python script/run_pipeline.py --config <profile> --dataset <dataset> \
--output-dir output/<new-run> --foundation-stereo-model <engine-path> \
--depth-backend commercial --no-depth-metricsOmit --no-depth-metrics when collected sensor depth is available and should be scored.
Start with --max-scenes 1 for a time/fit check before sizing a larger run.
For a capture with no pose ground truth:
./.venv/bin/python script/infer.py --config <profile> --dataset <dataset> \
--output-dir output/<new-run> --foundation-stereo-model <engine-path> \
--depth-backend commercialThe model path selects the backend. --depth-backend commercial asserts that selection; it
neither downloads a model nor establishes rights to the weights. Set the engine once in the
profile's overrides.depth.engine to avoid repeating the model-path flag.
Preserve existing results when comparing runs. Reuse cached depth only after checking its
metadata. --overwrite-results reruns segmentation and pose; --overwrite-depth additionally
regenerates depth. Regenerate depth after changes to the engine, rectified width, CLAHE,
working-distance bounds, or adapted data. Resume a pose-only failure without overwriting valid
depth. Working-distance bounds must be supplied together.
Do not repeat tuned defaults from config/defaults.yaml on every command; use profile overrides
for deliberate dataset-specific changes. Rebuild the engine if foundation_stereo_max_width
changes.
5. Re-score without repeating inference
For a rerank cutoff, IoU threshold, or visibility-band change, keep the completed predictions, mask sidecars, and depth files and run:
./.venv/bin/python script/evaluate.py --config <profile> --dataset <dataset> \
--run output/<completed-run> --output-dir output/<new-score-run> \
--rerank-cutoff 4.5 --no-depth-metricsOmit --no-depth-metrics when depth comparison is desired. A separate --output-dir preserves
the old report. No inference model is loaded; a GT cache miss can still require rasterization.
For an offline cutoff sweep, tools/sweep_rerank_cutoff.py --config <profile> --results-root output --datasets <dataset> expects one dataset subdirectory under the results root.
6. Verify provenance and interpret results
Inspect inference_config.json for the engine and max-width, and each scene's
depth/<scene>/metadata.json for backend: tao, normalization: imagenet, and model_fixed_hw.
Inspect both before trusting cached depth. These establish execution provenance, not legal approval.
Read report.md, pose_summary.json (overall, by_visibility, by_object), and
depth_summary.json when depth was scored. Compare:
matched_predictionsfirst: a change in matched population can bias apparent accuracy gains.max_vertex_error_within_threshold_rateagainst the configured threshold and required rate.Median, p90, and p99 vertex error, ADD/ADD-S, and rotation error, including per-object results.
Depth error on object pixels; whole-image error can be dominated by the table or background.
A higher success rate can coexist with a worse mean or tail. Report both, along with population changes. Preserve a baseline before overwriting results; use separate run directories when retaining predictions and provenance matters.
7. Batch runs
./.venv/bin/python script/run_batch_eval.py --config <profile> --output-root output/<batch-run> \
--foundation-stereo-model <engine-path> --depth-backend commercial --no-depth-metricsThe no-depth flag also removes collected-depth filtering from batch dataset discovery. Add
--continue-on-error only when failed datasets should not stop the sweep. Run GPU datasets
sequentially; inspect run_status.jsonl before interpreting aggregate summary.json or report.md.
Examples
"Adapt T-LESS and run one scene with the TAO depth engine."
"Re-score this finished FoundationPose run at cutoff 4.5 and preserve the old report."
"Compare these pose summaries; did the 5 mm success rate improve at the same coverage?"
Troubleshooting and limitations
A smoke check demonstrates backend operation, not pose accuracy. Accuracy requires a real
representative dataset and a retained baseline. Never report an unavailable metric as zero.
invalid resource handle points to pycuda context boundaries around TAO calls. Plausible depth
at roughly twice the expected scale calls for checking input normalization and calibration.
Report the command, dataset/profile, output paths, model provenance, completion status,
headline metrics with matched counts, and any unverified steps.
Files
- ATTRIBUTION.mdAdded by AutoGineer931 B
- BENCHMARK.md7.4 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- SKILL.md9.5 KiB
- agents/openai.yaml261 B
- evals/config.yml149 B
- evals/evals.json4.9 KiB
- evals/files/baseline.json275 B
- evals/files/candidate.json276 B
- skill-card.md3.9 KiB
- skill.oms.sig5.4 KiB
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Attribution and licence
- Created by
- zwdoescode
- Publisher
- NVIDIA (Official)
- Distributed by
- NVIDIA
- Source
- NVIDIA/skills · skills/foundationpose-pipeline · commit fce3cca
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE-APACHE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
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The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download foundationpose-pipeline.zip