# NVlabs/BundleSDF

[CVPR 2023] BundleSDF: Neural 6-DoF Tracking and 3D Reconstruction of Unknown Objects

Repository: https://github.com/NVlabs/BundleSDF
Canonical: https://ross.abutalabs.com/products/bundlesdf
Homepage: https://bundlesdf.github.io/
Language: Python
License: NOASSERTION
License Family: other
Last push: 2026-05-01T20:30:49+00:00

## Health v2 (maintenance only)
Score: 65/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 80, release rhythm 35, longevity 84
- inputs: {"age_days": 1177, "days_push": 124, "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 1410, forks 169 (observed 2026-08-28T04:04:38.836631+00:00)

## What it is
BundleSDF is a CVPR 2023 research implementation from NVIDIA for near real-time 6-DoF pose tracking of unknown rigid objects from monocular RGBD video, jointly performing neural 3D reconstruction via a Neural Object Field. It requires only a first-frame segmentation mask and combines feature matching, pose graph optimization, and a dynamic memory pool of posed frames.

## Use cases
- track 6-DoF pose of an unknown object from RGBD video
- reconstruct 3D geometry and appearance of untextured objects
- run object tracking under occlusion and large pose changes
- benchmark on HO3D, YCBInEOAT, and BEHAVE datasets
- generate neural object fields for robotics manipulation research

## When to choose
- you need to track arbitrary rigid objects without CAD models or prior training
- you have monocular RGBD input and only a first-frame segmentation
- you want state-of-the-art joint pose tracking and neural 3D reconstruction
- you are doing robotics or computer-vision research on object pose estimation

## When to avoid
- you need a production-ready supported product rather than research code
- you lack a GPU or cannot use the Docker environment
- you have no RGBD data and only monocular RGB
- you need real-time performance on embedded hardware

## Facets
- artifact type: library
- maturity: stable
- function: computer-vision, machine-learning, graphics, simulation
- domain: computer-vision, robotics, machine-learning
- platform: python
- tags: 6dof-pose-tracking, neural-object-field, rgbd, 3d-reconstruction, slam, cvpr-2023, research-code, linux, docker, gpu

## Member repositories
- NVlabs/BundleSDF (main) score 65

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:38.836631+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 2026-08-30T04:38:27.224175+00:00, confidence not recorded.
  - readme: https://github.com/NVlabs/BundleSDF (fetched 2026-08-28T04:04:38.836631+00:00, sha 7c9ce23440ef)
  - homepage: https://bundlesdf.github.io/ (fetched 2026-08-29T11:51:48.057021+00:00, sha d47fe9817caf)
- Data as of 2026-08-30T08:39:29.467469+00:00.