# CUT3R/CUT3R

Official implementation of Continuous 3D Perception Model with Persistent State

Repository: https://github.com/CUT3R/CUT3R
Canonical: https://ross.abutalabs.com/products/cut3r
Homepage: https://cut3r.github.io/
Language: Python
License: NOASSERTION
License Family: other
Last push: 2025-08-27T15:30:07+00:00

## Health v2 (maintenance only)
Score: 38/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 39, release rhythm 35, longevity 41
- inputs: {"age_days": 583, "days_push": 371, "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 1486, forks 89 (observed 2026-08-28T04:04:51.811119+00:00)

## What it is
CUT3R is the official PyTorch implementation of 'Continuous 3D Perception Model with Persistent State' (CVPR 2025 Oral), a stateful recurrent transformer that processes streams of RGB images and produces metric-scale pointmaps in a shared coordinate system. It supports online dense 3D scene reconstruction, dynamic scene reconstruction, and probing of unobserved scene regions from video or unordered photo collections.

## Use cases
- reconstruct 3D scenes from a stream of RGB images
- estimate per-pixel 3D pointmaps from video frames online
- reconstruct dynamic scenes without per-video optimization
- predict depth and camera pose from image sequences
- infer unseen regions of a scene from virtual views
- run multi-view stereo reconstruction on photo collections
- evaluate 3D/4D perception models on benchmark datasets

## When to choose
- you need feed-forward online 3D reconstruction from RGB-only inputs
- you want a unified model handling static and dynamic scenes
- you need metric-scale pointmaps in a common coordinate system
- you are doing research on stateful 3D perception or 4D reconstruction

## When to avoid
- you need a production-ready 3D scanning pipeline with SLAM guarantees
- you lack a CUDA-capable GPU for inference
- you need a lightweight real-time mobile 3D solution
- you require permissive licensing for commercial use (license is non-standard)

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, deep-learning, computer-vision, image-processing, simulation
- domain: computer-vision, deep-learning, machine-learning, artificial-intelligence
- platform: python, cross-platform
- tags: 3d-reconstruction, pointmap, stateful-model, transformer, cvpr-2025, research-code, depth-estimation, pose-estimation, video, pytorch, gpu, linux

## Member repositories
- CUT3R/CUT3R (main) score 38

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:51.811119+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 2026-08-30T04:33:49.675634+00:00, confidence not recorded.
  - readme: https://github.com/CUT3R/CUT3R (fetched 2026-08-28T04:04:51.811119+00:00, sha 8a189685b82c)
  - homepage: https://cut3r.github.io/ (fetched 2026-08-29T11:39:54.276362+00:00, sha 9a05b718c08f)
- Data as of 2026-08-30T08:39:29.467469+00:00.