SMFormer achieves state-of-the-art self-supervised stereo matching by using vision foundation models for disturbance-resistant features and data augmentation to enforce output consistency, rivaling or exceeding some supervised methods on benchmarks including Booster.
Croco v2: Improved cross-view completion pre-training for stereo matching and optical flow
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SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation
SMFormer achieves state-of-the-art self-supervised stereo matching by using vision foundation models for disturbance-resistant features and data augmentation to enforce output consistency, rivaling or exceeding some supervised methods on benchmarks including Booster.