Abstract
This paper presents a robust and automatic approach to photometric stereo, where the two main components, namely surface normals and visible surfaces, are respectively optimized by Expectation Maximization (EM). A dense set of input images is conveniently captured using a digital video camera while a handheld spotlight is being moved around the target object and a small mirror sphere. In our approach, the inherently complex optimization problem is simplified into a two-step optimization, where EM is employed in each step: 1) Using the dense input, the weight or importance of each observation is alternately optimized with the normal and albedo at each pixel and 2) using the optimized normals and employing the Markov Random Fields (MRFs), surface integrabilities and discontinuities are alternately optimized in visible surface reconstruction. Our mathematical derivation gives simple updating rules for the EM algorithms, leading to a stable, practical, and parameter-free implementation that is very robust even in the presence of complex geometry, shadows, highlight, and transparency. We present high-quality results on normal and visible surface reconstruction, where fine geometric details are automatically recovered by our method.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IEEE Transactions on Pattern Analysis and Machine Intelligence
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
