Dynamic Shape Capture via Periodical-Illumination Optical Flow Estimation and Multi-view Photometric Stereo

Abstract

Multi-view photometric stereo is well established for the shape recovery of static objects. However, it is difficult to align motion images under varying illumination so as to perform photometric stereo reconstruction for dynamic objects. To tackle this issue, this paper presents an optical flow estimation approach which works under periodically varying illuminations, and in cooperation with photometric stereo, enables high-quality 3D reconstruction of dynamic objects. Firstly, multi-view images of the moving object are captured under periodically varying illumination by the multi-camera multi-light system. Then, the optical flow is estimated to synthesize images under different illuminations for each viewpoint. Finally, the multi-view photometric stereo technique is employed to get a high accurate 3D model for each time instant. Experimental results on motion actors demonstrate that temporal successive images under varying illuminations are effectively registered, permitting accurate photometric reconstruction for moving objects.