NVlabs/FoundationStereo

Health v2 · maintenance only

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Adoption not part of the score

2874 stars · 284 forks observed · 2026-08-28

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

FoundationStereo is NVIDIA's official PyTorch implementation of a foundation model for zero-shot stereo depth estimation, published as a CVPR 2025 Oral (Best Paper Nomination). It takes a pair of stereo images and outputs a dense disparity map convertible to metric-scale depth maps or 3D point clouds, with strong cross-domain generalization.

Use cases

• estimate depth from stereo image pairs
• generate 3D point clouds from stereo cameras
• run zero-shot stereo matching on new domains without fine-tuning
• deploy depth estimation on Jetson with TensorRT
• benchmark stereo depth models on Middlebury and ETH3D
• extract dense disparity maps for robotics perception

When to choose

• you need state-of-the-art stereo depth estimation that generalizes zero-shot across domains
• you want a pretrained model that works without per-domain fine-tuning
• you need metric-scale depth or point clouds from stereo rigs
• you target embedded deployment via ONNX/TensorRT on Jetson

When to avoid

• you only have a single monocular camera instead of a stereo pair
• you need real-time inference on limited hardware without the Fast variant
• you require a permissive license for commercial use without checking terms
• you work outside GPU-capable environments

Facets

library · maturity active

computer-vision image-processing machine-learning deep-learning computer-vision deep-learning robotics artificial-intelligence python cpp stereo-matching depth-estimation disparity-map zero-shot-generalization foundation-model 3d-reconstruction point-cloud onnx tensorrt cvpr-2025 gpu linux

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