A 3D Imaging Framework Based on High-Resolution Photometric-Stereo and Low-Resolution Depth
International Journal of Computer Vision
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Given a dense set of imperfect normals obtained by photometric stereo or shape from shading, this paper presents an optimization algorithm which alternately optimizes until convergence the surface integrabilities and discontinuities inherent in the normal field, in order to derive a segmented surface description of the visible scene without noticeable distortion. In our Expectation-Maximization (EM) framework, we enforce discontinuity-preserving integrability so that fine details are preserved within each output segment while the occlusion boundaries are localized as sharp surface discontinuities. Using the resulting weighted discontinuity map, the estimation of a discontinuity-preserving height field can be formulated into a convex optimization problem. We compare our method and present convincing results on synthetic and real data.