# NVlabs/FoundationPose

[CVPR 2024 Highlight] FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects

Repository: https://github.com/NVlabs/FoundationPose
Canonical: https://ross.abutalabs.com/products/foundationpose
Homepage: https://nvlabs.github.io/FoundationPose/
Language: Python
License: NOASSERTION
License Family: other
Last push: 2026-04-29T16:53:08+00:00

## Health v2 (maintenance only)
Score: 62/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 79, release rhythm 35, longevity 71
- inputs: {"age_days": 995, "days_push": 126, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3516, forks 525 (observed 2026-08-28T04:08:07.857395+00:00)

## What it is
FoundationPose is NVIDIA's unified foundation model for 6D object pose estimation and tracking of novel objects, supporting both model-based (CAD model) and model-free (reference images) setups without fine-tuning. It ranked first on the BOP leaderboard for model-based novel object pose estimation and is the official CVPR 2024 Highlight implementation.

## Use cases
- estimate 6D pose of a novel object from RGBD images without retraining
- track unknown objects in video using only a few reference images
- enable robot grasping by localizing objects in 3D
- run pose estimation for AR applications
- benchmark pose estimation on the BOP leaderboard
- perform model-free 6DoF object tracking and 3D reconstruction

## When to choose
- you need zero-shot pose estimation for objects without task-specific training
- you have RGBD input and either a CAD model or a handful of reference images
- you want state-of-the-art accuracy on novel object pose benchmarks
- you are building robotic manipulation or AR pipelines needing object localization

## When to avoid
- you only have monocular RGB without depth
- you need real-time inference on embedded hardware without the Isaac ROS TensorRT variant
- you need a lightweight model for CPU-only deployment
- your objects are textureless planar markers better handled by fiducial systems like ArUco

## Facets
- artifact type: library
- maturity: active
- function: computer-vision, machine-learning, deep-learning
- domain: computer-vision, robotics, artificial-intelligence, autonomous-vehicles
- platform: python
- tags: 6d-pose-estimation, object-tracking, rgb-d, foundation-model, cvpr-2024, nvidia, robotics-perception, ar, linux, gpu

## Member repositories
- NVlabs/FoundationPose (main) score 62

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:07.857395+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:35:34.782184+00:00, confidence not recorded.
  - readme: https://github.com/NVlabs/FoundationPose (fetched 2026-08-28T04:08:07.857395+00:00, sha 7b9255afc570)
  - homepage: https://nvlabs.github.io/FoundationPose/ (fetched 2026-08-29T09:29:24.645688+00:00, sha f77d179bed51)
- Data as of 2026-08-30T08:39:29.467469+00:00.