Lambertian Reflectance and Linear Subspaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Efficiently combining positions and normals for precise 3D geometry
ACM SIGGRAPH 2005 Papers
Passive Photometric Stereo from Motion
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Poisson surface reconstruction
SGP '06 Proceedings of the fourth Eurographics symposium on Geometry processing
Multiview Stereo via Volumetric Graph-Cuts and Occlusion Robust Photo-Consistency
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
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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 multicamera 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.