Template Deformation-Based 3-D Reconstruction of Full Human Body Scans From Low-Cost Depth Cameras.
Full human body shape scans provide valuable data for a variety of applications including anthropometric surveying, clothing design, human-factors engineering, health, and entertainment. However, the high price, large volume, and difficulty of operating professional 3-D scanners preclude their use in home entertainment. Recently, portable low-cost red green blue-depth cameras such as the Kinect have become popular for computer vision tasks. However, the infrared mechanism of this type of camera leads to noisy and incomplete depth images. We construct a stereo full-body scanning environment composed of multiple depth cameras and propose a novel registration algorithm. Our algorithm determines a segment constrained correspondence for two neighboring views, integrating them using rigid transformation. Furthermore, it aligns all of the views based on uniform error distribution. The generated 3-D mesh model is typically sparse, noisy, and even with holes, which makes it lose surface details. To address this, we introduce a geometric and topological fitting prior in the form of a professionally designed high-resolution template model. We formulate a template deformation optimization problem to fit the high-resolution model to the low-quality scan. Its solution overcomes the obstacles posed by different poses, varying body details, and surface noise. The entire process is free of body and template markers, fully automatic, and achieves satisfactory reconstruction results.