A Framework for an Accurate Point Cloud Based Registration of Full 3D Human Body Scans

A Framework for an Accurate Point Cloud Based Registration of Full 3D Human Body Scans
Vladislav Golyanik, Gerd Reis, Bertram Taetz, Didier Stricker
Proceedings of IAPR International Conference on Machine Vision Applications IAPR Conference on Machine Vision Applications (MVA-17), March 8-12, Nagoya, Japan

Abstract:

Alignment of 3D human body scans is a challenging problem in computer vision with various applications. While being extensively studied for the mesh-based case, it is still involved if scans lack topology. In this paper, we propose a practical solution to the point cloud based registration of 3D human scans and a 3D human template. We adopt recent advances in point set registration with prior matches and design a fully automated registration framework. Our framework consists of several steps including establishment of prior matches, alignment of point clouds into a common reference frame, global non-rigid registration, partial non-rigid registration, and a post-processing step. We can handle large point clouds with significant variations in appearance automatically and achieve high registration accuracy which is shown experimentally. Finally, we demonstrate a pipeline for treatment of social pathologies with animatable virtual avatars as an exemplary real-world application of the new framework.