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
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.