Scalable Cage-Driven Feature Detection and Shape Correspondence for 3D Point Sets

We propose an automatic deformation-driven correspondence algorithm for 3D point sets of non-rigid articulated shapes. Our approach uses simple geometric cages to embed the point set data and extract and match a coarse set of prominent features. We seek feature correspondences which lead to low-distortion deformations of the cages while satisfying the feature pairing. Our approach operates on the simplified geometric domain of the cage instead of the more complex 3D point data. Thus, it is robust to noise, partial occlusions, and insensitive to non-regular sampling. We demonstrate the potential of our approach by finding pairwise correspondences for sequences of acquired time-varying 3D scan point data.