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CVPR2025Top-tier venue

Stereo Anywhere: Robust Zero-Shot Deep Stereo Matching Even Where Either Stereo or Mono Fail

Luca Bartolomei, Fabio Tosi, Matteo Poggi, Stefano Mattoccia

2025Year
23Top-tier citations

Abstract

https://stereoanywhere.github.io/ RGB Depth Anything v2 [121] RAFT-Stereo [55] Stereo Anywhere (Ours) Middlebury ✓ ✓ ✓ Booster ✓ ✗ ✓ MonoTrap ✗ ✓ ✓ Figure 1. Stereo Anywhere: Combining Monocular and Stereo Strenghts for Robust Depth Estimation. Our model achieves accurate results on standard conditions (on Middlebury [86]), while effectively handling non-Lambertian surfaces where stereo networks fail (on Booster [127]) and perspective illusions that deceive monocular depth foundation models (on MonoTrap, our novel dataset).

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