# NVlabs/Fast-FoundationStereo

[CVPR 2026] Fast-FoundationStereo: Real-Time Zero-Shot Stereo Matching

Repository: https://github.com/NVlabs/Fast-FoundationStereo
Canonical: https://ross.abutalabs.com/products/fast-foundationstereo
Homepage: https://nvlabs.github.io/Fast-FoundationStereo/
Language: Python
License: NOASSERTION
License Family: other
Last push: 2026-05-26T20:22:06+00:00

## Health v2 (maintenance only)
Score: 54/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 84, release rhythm 35, longevity 19
- inputs: {"age_days": 272, "days_push": 99, "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 1432, forks 143 (observed 2026-08-28T04:04:42.994756+00:00)

## What it is
Fast-FoundationStereo is NVIDIA's official PyTorch implementation of a real-time zero-shot stereo matching model family, accepted to CVPR 2026. It distills and prunes the FoundationStereo model to run over 10x faster while closely matching its zero-shot accuracy for disparity/depth estimation from stereo image pairs.

## Use cases
- estimate depth from stereo image pairs in real time
- run zero-shot stereo matching without per-domain fine-tuning
- compute disparity maps for robotics or AR applications
- trade off speed versus accuracy with model variants
- benchmark real-time stereo matching methods

## When to choose
- you need real-time stereo depth estimation with strong generalization to unseen domains
- you want a drop-in faster alternative to FoundationStereo for online inference
- you have a CUDA GPU and want state-of-the-art zero-shot disparity results

## When to avoid
- you need the absolute highest offline accuracy and latency does not matter (use FoundationStereo instead)
- you need CPU-only inference
- you need a production-supported library with a stable API and permissive license

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, deep-learning, computer-vision, image-processing
- domain: computer-vision, machine-learning, deep-learning, robotics
- platform: python
- tags: stereo-matching, depth-estimation, disparity, zero-shot, real-time, knowledge-distillation, neural-architecture-search, research-code, cvpr, gpu, linux, docker

## Member repositories
- NVlabs/Fast-FoundationStereo (main) score 54

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:42.994756+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 2026-08-30T04:37:00.107810+00:00, confidence not recorded.
  - readme: https://github.com/NVlabs/Fast-FoundationStereo (fetched 2026-08-28T04:04:42.994756+00:00, sha 01dd1dd5af73)
  - homepage: https://nvlabs.github.io/Fast-FoundationStereo/ (fetched 2026-08-29T11:48:07.031166+00:00, sha 3468c303cd5b)
- Data as of 2026-08-30T08:39:29.467469+00:00.