Memory-Scalable and Simplified Functional Map Learning

arXiv:2404.00330v1 Announce Type: new
Abstract: Deep functional maps have emerged in recent years as a prominent learning-based framework for non-rigid shape matching problems. While early methods in this domain only focused on learning in the functional domain, the latest techniques have demonstrated that by promoting consistency between functional and pointwise maps leads to significant improvements in accuracy. Unfortunately, existing approaches rely heavily on the computation of large dense matrices arising from soft pointwise maps, which compromises their efficiency and scalability. …

abstract arxiv cs.ai cs.cv domain framework functional leads map maps memory scalable simplified type