Exact Recovery with Symmetries for Procrustes Matching

TL;DR

This paper analyzes the convex relaxation approach for Procrustes matching, showing it can exactly recover solutions in noise-free cases, including symmetric shapes, and introduces a method to handle multiple solutions.

Contribution

It provides the first theoretical analysis demonstrating exact recovery with symmetries in Procrustes matching using convex relaxation.

Findings

PM-SDP correctly solves exact PM for asymmetric shapes.

The relaxation's solutions are convex combinations of PM solutions.

A random algorithm can recover all solutions with high probability.

Abstract

The Procrustes matching (PM) problem is the problem of finding the optimal rigid motion and labeling of two point sets so that they are as close as possible. Both rigid and non-rigid shape matching problems can be formulated as PM problems. Recently [Maron et al.] presented a novel convex semi-definite programming relaxation (PM-SDP) for PM which achieves state of the art results on common shape matching benchmarks. In this paper we analyze the successfulness of PM-SDP in solving PM problems without noise (Exact PM problems). We begin by showing Exact PM to be computationally equivalent to the graph isomorphism problem. We demonstrate some natural theoretical properties of the relaxation, and use these properties together with the moment interpretation of [Lasserre] to show that for exact PM problems and for (generic) input shapes which are asymmetric or bilaterally symmetric, the relaxation returns a correct solution of PM. For symmetric shapes, PM has multiple solutions. The non-convex set of optimal solutions of PM is strictly contained in the convex set of optimal solutions of PM-SDP, so that `most' solutions of PM-SDP will not be solutions of PM. We deal with this by showing the solution set of PM to be the extreme points of the solution set of PM-SDP, and suggesting a random algorithm which returns a solution of PM with probability one, and returns all solutions of PM with equal probability. We also show these results can be extended to the almost-exact case. To the best of our knowledge, our work is the first to achieve exact recovery in the presence of multiple solutions.