Multiway Non-rigid Point Cloud Registration via Learned Functional Map Synchronization

TL;DR

SyNoRiM introduces a robust, flexible framework for jointly registering multiple non-rigid shapes by synchronizing learned functional maps, effectively handling noise, occlusions, and multi-body deformations.

Contribution

It proposes a novel method that learns basis functions and synchronizes pairwise functional maps into a cycle-consistent whole for improved non-rigid shape registration.

Findings

Achieves state-of-the-art registration accuracy

Handles both non-rigid and multi-body cases efficiently

Avoids costly point-wise permutation optimization

Abstract

We present SyNoRiM, a novel way to jointly register multiple non-rigid shapes by synchronizing the maps relating learned functions defined on the point clouds. Even though the ability to process non-rigid shapes is critical in various applications ranging from computer animation to 3D digitization, the literature still lacks a robust and flexible framework to match and align a collection of real, noisy scans observed under occlusions. Given a set of such point clouds, our method first computes the pairwise correspondences parameterized via functional maps. We simultaneously learn potentially non-orthogonal basis functions to effectively regularize the deformations, while handling the occlusions in an elegant way. To maximally benefit from the multi-way information provided by the inferred pairwise deformation fields, we synchronize the pairwise functional maps into a cycle-consistent whole thanks to our novel and principled optimization formulation. We demonstrate via extensive experiments that our method achieves a state-of-the-art performance in registration accuracy, while being flexible and efficient as we handle both non-rigid and multi-body cases in a unified framework and avoid the costly optimization over point-wise permutations by the use of basis function maps.