Detecting discontinuities for surface reconstruction

Photometric stereo algorithms produce a map of normal directions from the input images. The 3D surface can be reconstructed from this normal map. Existing surface reconstruction works often assume the normal map is integrable but contaminated by small scale non-integrable noise. However, real surfaces often contain large discontinuities such as occlusion boundaries and sharp depth changes, which break the integrable assumption commonly made in many works. Here, we propose a method to detect these discontinuities by combining multiple geometric cues with trained classifiers and a simple graph optimization. The surface is then reconstructed with the guidance of these detected discontinuities. Experiments show our method outperforms existing works.