Photo-consistent surfaces from a sparse set of viewpoints

We address the problem of reconstructing 3D shapes from color data available in a sparse set of views from all directions of a scene. As an advantage when compared to multiview stereo approaches, our method is able to reconstruct object surfaces from a small number of views in wide-baseline setups. This introduces a trade-off between reconstruction accuracy and spatial coverage. The proposed algorithm obtains candidate surface points using a photo-consistency test and restricting the analysis to foreground pixels. The final surface points are extracted by iteratively carving away candidate points that are not photo-consistent with the complete multiview set. Finally, a surface patch is obtained from each colored surface point by estimating its orientation. Experimental results reflect the validity of the approach by comparing it to a voxelized implementation.