Reliable Isometric Point Correspondence from Depth

Abstract

We propose a new iterative isometric point correspondence method that relies on diffusion distance to handle challenges posed by commodity depth sensors, which usually provide incomplete and noisy surface data exhibiting holes and gaps. We formulate the correspondence problem as finding an optimal partial mapping between two given point sets, that minimizes deviation from isometry. Our algorithm starts with an initial rough correspondence between keypoints, obtained via a standard descriptor matching technique. This initial correspondence is then pruned and updated by iterating a perfect matching algorithm until convergence to find as many reliable correspondences as possible. For shapes with intrinsic symmetries such as human models, we additionally provide a symmetry aware extension to improve our formulation. The experiments show that our method provides state of the art performance over depth frames exhibiting occlusions, large deformations and topological noise.