Integrating Efficient Optimal Transport and Functional Maps For Unsupervised Shape Correspondence Learning

arXiv:2403.01781v1 Announce Type: new
Abstract: In the realm of computer vision and graphics, accurately establishing correspondences between geometric 3D shapes is pivotal for applications like object tracking, registration, texture transfer, and statistical shape analysis. Moving beyond traditional hand-crafted and data-driven feature learning methods, we incorporate spectral methods with deep learning, focusing on functional maps (FMs) and optimal transport (OT). Traditional OT-based approaches, often reliant on entropy regularization OT in learning-based framework, face computational challenges due to their quadratic cost. Our …

abstract analysis applications arxiv beyond computer computer vision cs.ai cs.cv data data-driven feature functional graphics maps moving pivotal registration statistical texture tracking transfer transport type unsupervised vision