SIGMA: Scale-Invariant Global Sparse Shape Matching
Authored by Maolin Gao, Paul Roetzer, Marvin Eisenberger, Zorah Lähner, Michael Moeller, Daniel Cremers, Florian Bernard
Published in International Conference on Computer Vision (ICCV) 2023
Abstract
We propose a novel mixed-integer programming (MIP) formulation for generating precise sparse correspondences for highly non-rigid shapes. To this end, we introduce a projected Laplace-Beltrami operator (PLBO) which combines intrinsic and extrinsic geometric information to measure the deformation quality induced by predicted correspondences. We integrate the PLBO, together with an orientation-aware regulariser, into a novel MIP formulation that can be solved to global optimality. In contrast to previous methods, our approach is provably invariant to rigid transformations and global scaling, intialisation-free, has optimality guarantees, and scales to high resolution meshes with (empirically observed) linear time. We show state-of-the-art results for sparse non-rigid matching on several challenging 3D datasets, including inconsistent meshing, as well as applications in mesh-to-point-cloud matching.
Resources
Bibtex
@inproceedings{ gao2023sigma,
author = { Maolin Gao and Paul Roetzer and Marvin Eisenberger and Zorah Lähner and Michael Moeller and Daniel Cremers and Florian Bernard },
title = { SIGMA: Scale-Invariant Global Sparse Shape Matching },
booktitle = { International Conference on Computer Vision (ICCV) },
year = { 2023 },
}