Registration of Human Point Set using Automatic Key Point Detection and Region-aware Features

Abstract

Non-rigid point set registration is challenging when point sets have large deformations and different numbers of points. Examples of such point sets include human point sets representing complex human poses captured by different types of depth cameras. In this work, we present a probabilistic, non-rigid registration method to deal with these issues. Two regularization terms are used: key point correspondences and local neighborhood preservation. Our method detects key points in the point sets based on geodesic distance. Correspondences are established using a new cluster-based, region-aware feature descriptor. This feature descriptor encodes the association of a cluster to the left-right (symmetry) or upper-lower regions of the point sets. We use the Stochastic Neighbor Embedding (SNE) constraint to preserve the local neighborhood of the point set. Experimental results on challenging 3D human poses demonstrate that our method outperforms the state-of-the-art methods. Our method achieved highly competitive performance with a slight increase of error by 3.9% in comparison with the method using manually specified key point correspondences.