Diffusion-based isometric depth correspondence

We propose an 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 any point matching technique. This initial correspondence is then pruned and updated by iterating a perfect matching algorithm until convergence in order to find as many reliable correspondences as possible. The resulting set of sparse but reliable correspondences then serves as a base matching from which a dense correspondence set is estimated. We additionally provide a global intrinsic symmetry detection technique which clusters a point cloud into its symmetric sides. We incorporate this technique into our point-based correspondence method so as to address the symmetrical flip problem and to further improve the reliability of our matching results. Our symmetry-aware correspondence method is especially effective on human shapes with global reflectional symmetry. We hence conduct experiments on datasets comprising human shapes and show that our method provides state of the art performance over depth frames exhibiting occlusions, large deformations, and topological noise.