Cross-View Completion Models are Zero-shot Correspondence Estimators

Honggyu An, Jin Hyeon Kim, Seonghoon Park, Jaewoo Jung, Jisang Han, Sunghwan Hong, Seungryong Kim

Abstract: In this work, we analyze new aspects of cross-view completion, mainly through the analogy of cross-view completion and traditional self-supervised correspondence learning algorithms. Based on our analysis, we reveal that the cross-attention map of Croco-v2, best reflects this correspondence information compared to other correlations from the encoder or decoder features. We further verify the effectiveness of the cross-attention map by evaluating on both zero-shot and supervised dense geometric correspondence and multi-frame depth estimation.

External IDs:dblp:conf/cvpr/AnK0JHHK25