Non-Rigid Shape Registration via Deep Functional Maps Prior

TL;DR

This paper introduces a deep learning framework for non-rigid shape registration that combines spectral embedding with dynamic, robust correspondence updates, achieving state-of-the-art results without requiring correspondence supervision.

Contribution

It presents a novel approach that integrates deep functional maps with a deformable registration process, robustly handling large intrinsic and extrinsic deformations without correspondence supervision.

Findings

Achieves state-of-the-art performance on non-rigid point cloud matching benchmarks.

Effectively handles shapes with significant intrinsic and extrinsic deformations.

Requires only limited variability in training shapes for high-quality registration.

Abstract

In this paper, we propose a learning-based framework for non-rigid shape registration without correspondence supervision. Traditional shape registration techniques typically rely on correspondences induced by extrinsic proximity, therefore can fail in the presence of large intrinsic deformations. Spectral mapping methods overcome this challenge by embedding shapes into, geometric or learned, high-dimensional spaces, where shapes are easier to align. However, due to the dependency on abstract, non-linear embedding schemes, the latter can be vulnerable with respect to perturbed or alien input. In light of this, our framework takes the best of both worlds. Namely, we deform source mesh towards the target point cloud, guided by correspondences induced by high-dimensional embeddings learned from deep functional maps (DFM). In particular, the correspondences are dynamically updated according to the intermediate registrations and filtered by consistency prior, which prominently robustify the overall pipeline. Moreover, in order to alleviate the requirement of extrinsically aligned input, we train an orientation regressor on a set of aligned synthetic shapes independent of the training shapes for DFM. Empirical results show that, with as few as dozens of training shapes of limited variability, our pipeline achieves state-of-the-art results on several benchmarks of non-rigid point cloud matching, but also delivers high-quality correspondences between unseen challenging shape pairs that undergo both significant extrinsic and intrinsic deformations, in which case neither traditional registration methods nor intrinsic methods work. The code is available at https://github.com/rqhuang88/DFR.