Visible Surface Reconstruction from Normals with Discontinuity Consideration

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.

[1]  Brendan J. Frey,et al.  Enforcing integrability for surface reconstruction algorithms using belief propagation in graphical models , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[2]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  B. Karacali,et al.  Partial integrability in surface reconstruction from a given gradient field , 2002, Proceedings. International Conference on Image Processing.

[4]  Berthold K. P. Horn Height and gradient from shading , 1989, International Journal of Computer Vision.

[5]  Kuo-Chin Fan,et al.  Wavelet-based shape from shading , 1994, Proceedings of 1st International Conference on Image Processing.

[6]  Tai-Pang Wu,et al.  Dense Photometric Stereo by Expectation Maximization , 2006, ECCV.

[7]  Demetri Terzopoulos,et al.  The Computation of Visible-Surface Representations , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Tien-Tsin Wong,et al.  Dense photometric stereo using tensorial belief propagation , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[9]  Robert J. Woodham,et al.  Photometric method for determining surface orientation from multiple images , 1980 .

[10]  Rama Chellappa,et al.  A Method for Enforcing Integrability in Shape from Shading Algorithms , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Wesley E. Snyder,et al.  Reconstructing discontinuous surfaces from a given gradient field using partial integrability , 2003, Comput. Vis. Image Underst..

[12]  Jeff A. Bilmes,et al.  A gentle tutorial of the em algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models , 1998 .

[13]  Steven M. Seitz,et al.  Shape and Spatially-Varying BRDFs from Photometric Stereo , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Tai-Pang Wu,et al.  Dense photometric stereo using a mirror sphere and graph cut , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[15]  Jean Ponce,et al.  Computer Vision: A Modern Approach , 2002 .

[16]  Andrew Blake,et al.  Visual Reconstruction , 1987, Deep Learning for EEG-Based Brain–Computer Interfaces.

[17]  Peter Kovesi,et al.  Shapelets correlated with surface normals produce surfaces , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.