EchoMatch: Partial-to-Partial Shape Matching via Correspondence Reflection

Yizheng Xie, Viktoria Ehm, Paul Roetzer, Nafie El Amrani, Maolin Gao, Florian Bernard, Daniel Cremers; Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR), 2025, pp. 11665-11675

Finding correspondences between 3D shapes is a crucial problem in computer vision and graphics. While most research has focused on finding correspondences in settings where at least one of the shapes is complete, the realm of partial-to-partial shape matching remains under-explored. Yet, it is important since in many applications shapes are only observed partially due to occlusion or scanning. Finding correspondences between partial shapes comes with an additional challenge: We not only want to identify correspondences between points on either shape but also have to determine which points of each shape actually have a partner. To tackle this challenging problem, we present EchoMatch, a novel framework for partial-to-partial shape matching that incorporates the concept of correspondence reflection to enable an overlap prediction within a functional map framework. With this approach, we show that we can outperform current SOTA methods in challenging partial-to-partial shape matching problems. Our code is available at https://echo-match.github.io.