A robust approach based on photometric stereo for surface reconstruction

We present a new framework for surface reconstruction with technique of photometric stereo, which is based on advanced convex optimization technique. We firstly remove the errors in images by robust principle component analysis (RPCA), and then obtain low-rank matrix and surface normal field. Unlike previous approaches, this method uses all the available information to simultaneously fix missing and erroneous entries. The new technique is more computationally efficient and provides theoretical assurance for robustness to large errors. Experimental results demonstrate that this framework can improve the precision for surface reconstruction with noise.