Object surface recovery using a multi-light photometric stereo technique for non-Lambertian surfaces subject to shadows and specularities

This paper presents a new multi-light source photometric stereo system for reconstructing images of various characteristics of non-Lambertian rough surfaces with widely varying texture and specularity. Compared to the traditional three-light photometric stereo method, extra lights are employed using a hierarchical selection strategy to eliminate the effects of shadows and specularities, and to make the system more robust. We also show that six lights is the minimum needed in order to apply photometric stereo to the entire visible surface of any convex object. Experiments on synthetic and real scenes demonstrate that the proposed method can extract surface reflectance and orientation effectively, even in the presence of strong shadows and highlights. Hence, the method offers advantages in the recovery of dichromatic surfaces possessing rough texture or deeply relieved topographic features, with applications in reverse engineering and industrial surface inspection. Experimental results are presented in the paper.

[1]  Jiahua Wu,et al.  Rotation invariant classification of 3D surface texture using photometric stereo , 2003 .

[2]  Maria Petrou,et al.  The 4-Source Photometric Stereo Technique for Three-Dimensional Surfaces in the Presence of Highlights and Shadows , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Rui J. P. de Figueiredo,et al.  A Theory of Photometric Stereo for a Class of Diffuse Non-Lambertian Surfaces , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Norimichi Tsumura,et al.  Estimating the Directions to Light Sources Using Images of Eye for Reconstructing 3D Human Face , 2003, CIC.

[5]  Berthold K. P. Horn Understanding Image Intensities , 1977, Artif. Intell..

[6]  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.

[7]  Katsushi Ikeuchi,et al.  Extracting the Shape and Roughness of Specular Lobe Objects Using Four Light Photometric Stereo , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  F. E. Nicodemus,et al.  Geometrical considerations and nomenclature for reflectance , 1977 .

[9]  Katsushi Ikeuchi,et al.  Object shape and reflectance modeling from observation , 1997, SIGGRAPH.

[10]  Gabriel Taubin,et al.  Appying Shape from Lighting Variation to Bump Map Capture , 1997, Rendering Techniques.

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

[12]  E. North Coleman,et al.  Obtaining 3-dimensional shape of textured and specular surfaces using four-source photometry , 1982, Comput. Graph. Image Process..

[13]  David J. Kriegman,et al.  Beyond Lambert: reconstructing specular surfaces using color , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).