Fuzzy Correspondences for Robust Shape Registration

Abhishek Kolagunda, Scott Sorensen, Philip Saponaro, Wayne Treible, Chandra Kambhamettu, Kelly D. Sherbondy

Abstract: Shape registration is the process of aligning one 3D model to another. Most previous methods to align shapes with no known correspondences attempt to solve for both the transformation and correspondences iteratively. We present a shape registration approach that solves for the transformation using fuzzy correspondences to maximize the overlap between the given shape and the target shape. A coarse to fine approach with Levenberg-Marquardt method is used for optimization. We show our approach is robust and outperforms other state of the art methods when point clouds are noisy, sparse, and have non-uniform density. Our approach is generic, and we illustrate this by showing that it can also be used for 2D-3D alignment. Experiments show that our method is more robust to initialization and can handle larger changes in scale and rotation than other methods.