Despite the success of deep functional maps in non-rigid 3D shape matching, there exists no learning framework that models both self-symmetry and shape matching simultaneously. This is despite the fact that errors due to symmetry mismatch are a major challenge in non-rigid shape matching. In this paper, we propose a novel framework that simultaneously learns both self symmetry as well as a pairwise map between a pair of shapes. Our key idea is to couple a self symmetry map and a pairwise map through a regularization term that provides a joint constraint on both of them, thereby, leading to more accurate maps. We validate our method on several benchmarks where it outperforms many competitive baselines on both tasks.
@article{arxiv.2112.02713,
title = {Joint Symmetry Detection and Shape Matching for Non-Rigid Point Cloud},
author = {Abhishek Sharma and Maks Ovsjanikov},
journal= {arXiv preprint arXiv:2112.02713},
year = {2022}
}
Comments
Under Review. arXiv admin note: substantial text overlap with arXiv:2110.02994