Fast Markov Random Field Optimisation for Topologically Noisy 3D Shape Matching
Authored by Paul Roetzer, Johan Thunberg, Zorah Lähner, Florian Bernard
Published in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2026
Abstract
In many real world applications of non-rigid shape matching, the shapes are subject to topological noise (i.e. varying genus). In this paper, we propose a novel formulation based on Markov Random Fields (MRF) that can handle these cases with topological noise. The solutions to our optimisation problem can be approximated efficiently using the alpha expansion algorithm, which gives rise to theoretical approximation guarantees. In particular, we cast non-rigid 3D shape matching as a multi-labelling problem in which each triangle of the source shape is assigned a label that represents the matching to a specific surface element on the target shape. We propose a novel pairwise term that imposes that our matching prefers solutions in which neighbouring triangles on the source shape remain close on the target shape. Further, by exploiting the specific structure of our label space, we show that the alpha expansion algorithm can be customised to gain significant speed-ups, while maintaining its approximation guarantees. We test our formalism on various shape matching datasets including settings in which shapes have topological artefacts.
Resources
Bibtex
@inproceedings{ roetzer2026markov,
author = { Paul Roetzer and Johan Thunberg and Zorah Lähner and Florian Bernard },
title = { Fast Markov Random Field Optimisation for Topologically Noisy 3D Shape Matching },
booktitle = { IEEE Conference on Computer Vision and Pattern Recognition (CVPR) },
year = { 2026 },
}