Multi-view reconstruction under varying illumination conditions

This paper addresses the problem of complete and detailed 3D model reconstruction of objects filmed by multiple cameras under varying illumination. Firstly, initial normal maps are obtained to enhance the correspondence mapping. Then, the depth for every pixel is estimated by combining photometric constraint with occlusion robust photo-consistency. Finally, after filtering the point cloud, a Poisson surface reconstruction is applied to obtain a watertight mesh. In contrast with traditional photometric stereo techniques, the proposed algorithm does not directly calculate the photometric normal but integrates the photometric constraint into the depth estimation. Furthermore, different from classic multi-view stereo (MVS), we consider the counterpart under changing light conditions. The algorithm has been implemented based on our multi-camera and multi-light acquisition system. We validate the method by complete reconstruction of challenging real objects and show experimentally that this technique can greatly improve on correspondence-based MVS results.