Efficient Point Cloud Matching for 3D Geometric Shape Assembly

Abstract

Learning to assemble geometric shapes into a larger target structure is a fundamental task with various high-level visual applications. In this work, we frame this problem as geometric registration with extremely low overlap. Our goal is to establish accurate correspondences on the mating surface of the shape fragments to predict their relative rigid transformations for assembly. To this end, we introduce Proxy Match Transform (PMT), an approximate high-order feature transform layer that enables reliable correspondences between dense point clouds of shape fragments, while incurring low costs in memory and compute. In our experiments, we demonstrate that Proxy Match Transform surpasses existing state-of-the-art baselines on a popular geometric shape assembly dataset, while exhibiting higher efficiency than other high-order feature transform methods.