Multi-scale Non-Rigid Point Cloud Registration Using Robust Sliced-Wasserstein Distance via Laplace-Beltrami Eigenmap

TL;DR

This paper introduces a multi-scale non-rigid point cloud registration method leveraging Laplace-Beltrami eigenmaps and robust sliced-Wasserstein distance, providing an efficient and accurate solution for complex geometric data alignment.

Contribution

It presents a novel multi-scale registration framework combining Laplace-Beltrami eigenmaps with optimal transport and sliced-Wasserstein distance for non-rigid point cloud alignment.

Findings

Effective multi-scale registration from coarse to fine scales

Robustness to non-rigid transformations and isometric invariance

Improved accuracy and efficiency over existing methods

Abstract

In this work, we propose computational models and algorithms for point cloud registration with non-rigid transformation. First, point clouds sampled from manifolds originally embedded in some Euclidean space $\mathbb{R}^D$ are transformed to new point clouds embedded in $\mathbb{R}^n$ by Laplace-Beltrami(LB) eigenmap using the $n$ leading eigenvalues and corresponding eigenfunctions of LB operator defined intrinsically on the manifolds. The LB eigenmap are invariant under isometric transformation of the original manifolds. Then we design computational models and algorithms for registration of the transformed point clouds in distribution/probability form based on the optimal transport theory which provides both generality and flexibility to handle general point clouds setting. Our methods use robust sliced-Wasserstein distance, which is as the average of projected Wasserstein distance along different directions, and incorporate a rigid transformation to handle ambiguities introduced by the Laplace-Beltrami eigenmap. By going from smaller $n$, which provides a quick and robust registration (based on coarse scale features) as well as a good initial guess for finer scale registration, to a larger $n$, our method provides an efficient, robust and accurate approach for multi-scale non-rigid point cloud registration.