Diffuse-specular separation of multi-view images under varying illumination

Separating diffuse and specular reflection components is important for preprocessing of various computer vision techniques such as photometric stereo. In this paper, we address diffuse-specular separation for photometric stereo based on light fields. Specifically, we reveal the low-rank structure of the multi-view images under varying light source directions, and then formulate the diffuse-specular separation as a low-rank approximation of the 3rd order tensor. Through a number of experiments using real images, we show that our proposed method, which integrates the complement clues based on varying light source directions and varying viewing directions, works better than existing techniques.

[1]  Harry Shum,et al.  Principal Component Analysis with Missing Data and Its Application to Polyhedral Object Modeling , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Hideki Hayakawa Photometric stereo under a light source with arbitrary motion , 1994 .

[3]  Yongtian Wang,et al.  Robust Photometric Stereo via Low-Rank Matrix Completion and Recovery , 2010, ACCV.

[4]  Mike J. Chantler,et al.  Can two specular pixels calibrate photometric stereo? , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[5]  Demetri Terzopoulos,et al.  Multilinear Analysis of Image Ensembles: TensorFaces , 2002, ECCV.

[6]  Katsushi Ikeuchi,et al.  Determining Reflectance Parameters and Illumination Distribution from a Sparse Set of Images for View-dependent Image Synthesis , 2001, ICCV.

[7]  Daniel Snow,et al.  Shape and albedo from multiple images using integrability , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

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

[9]  Steven A. Shafer,et al.  Using color to separate reflection components , 1985 .

[10]  In-So Kweon,et al.  Geometric Calibration of Micro-Lens-Based Light Field Cameras Using Line Features , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Terrance E. Boult,et al.  Constraining Object Features Using a Polarization Reflectance Model , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Jitendra Malik,et al.  Depth Estimation and Specular Removal for Glossy Surfaces Using Point and Line Consistency with Light-Field Cameras , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Amnon Shashua,et al.  On Photometric Issues in 3D Visual Recognition from a Single 2D Image , 2004, International Journal of Computer Vision.

[14]  P. Hanrahan,et al.  Light Field Photography with a Hand-held Plenoptic Camera , 2005 .

[15]  Athinodoros S. Georghiades,et al.  Incorporating the Torrance and Sparrow model of reflectance in uncalibrated photometric stereo , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.