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

MCML Authors

Viktoria Ehm

→ Group Daniel Cremers
Computer Vision & Artificial Intelligence

Maolin Gao

→ Group Daniel Cremers
Computer Vision & Artificial Intelligence

Daniel Cremers

Prof. Dr.

Director

Computer Vision & Artificial Intelligence

Abstract

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 of importance 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.

inproceedings