princeton-vl/RAFT-Stereo

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1119 stars · 186 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

RAFT-Stereo is a PyTorch implementation of a deep learning model for stereo matching that estimates disparity maps from stereo image pairs using multilevel recurrent field transforms. It includes training and evaluation code for standard stereo benchmarks like SceneFlow, Middlebury, ETH3D, and KITTI.

Use cases

• estimate depth from stereo image pairs
• compute disparity maps for 3D reconstruction
• run stereo matching on KITTI or Middlebury datasets
• train a stereo depth network on SceneFlow
• generate point clouds from stereo images
• benchmark stereo matching models on ETH3D

When to choose

• you need state-of-the-art stereo disparity estimation in Python
• you want a research-grade model with pretrained checkpoints and benchmark evaluation
• you have a CUDA GPU and stereo image pairs for depth estimation

When to avoid

• you need monocular depth estimation from a single image
• you need real-time stereo on CPU or embedded hardware without a GPU
• you want a plug-and-play production library rather than research code

Facets

library · maturity stable

computer-vision image-processing machine-learning deep-learning computer-vision machine-learning robotics autonomous-vehicles python stereo-matching depth-estimation disparity pytorch 3d-vision research-code gpu linux

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