Integration of a Normal Field without Boundary Condition

We show how to use two existing methods of integration of a normal eld in the absence of boundary condition, which makes them more realistic. Moreover, we show how perspective can be taken into account, in order to render the 3D-reconstruction more accurate. Finally, the joint use of both these methods of integration allows us to obtain very satisfactory results, from the point of view of CPU time as well as that of the accuracy of the reconstructions. As an application, we use this new combined method of integration of a normal eld in the framework of photometric stereo, a technique which aims at computing a normal field to the surface of a scene from several images of this scene illuminated from various directions. The performances of the proposed method are illustrated on synthetic, as well as on real images

[1]  Ira Kemelmacher-Shlizerman,et al.  Photometric Stereo with General, Unknown Lighting , 2006, International Journal of Computer Vision.

[2]  Jean-Denis Durou,et al.  Numerical methods for shape-from-shading: A new survey with benchmarks , 2008, Comput. Vis. Image Underst..

[3]  Rama Chellappa,et al.  Direct Analytical Methods for Solving Poisson Equations in Computer Vision Problems , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

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

[5]  Rama Chellappa,et al.  An algebraic approach to surface reconstruction from gradient fields , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[6]  Olivier D. Faugeras,et al.  Shape from shading: a well-posed problem? , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[7]  Chu-Song Chen,et al.  The 4-Source Photometric Stereo Under General Unknown Lighting , 2006, ECCV.

[8]  Nahum Kiryati,et al.  Photometric stereo under perspective projection , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[9]  Pascal Daniel Peut-on extraire le relief d'une seule image ? , 2000 .

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

[11]  Lingxiao Li,et al.  A line-integration based method for depth recovery from surface normals , 1988, Comput. Vis. Graph. Image Process..

[12]  Ping-Sing Tsai,et al.  Shape from Shading: A Survey , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Michael J. Brooks,et al.  The variational approach to shape from shading , 1986, Comput. Vis. Graph. Image Process..

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

[15]  Nahum Kiryati,et al.  Depth from gradient fields and control points: bias correction in photometric stereo , 2004, Image Vis. Comput..

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

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

[18]  Yee-Hong Yang,et al.  Shading Logic: A Heuristic Approach to Recover Shape from Shading , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Olivier D. Faugeras,et al.  "Perspective shape from shading" and viscosity solutions , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[20]  Lyle Noakes,et al.  Nonlinearities and Noise Reduction in 3-Source Photometric Stereo , 2004, Journal of Mathematical Imaging and Vision.

[21]  Stephen Lin,et al.  Resolution-Enhanced Photometric Stereo , 2006, ECCV.

[22]  José R. A. Torreão Geometric - photometric approach to monocular shape estimation , 2003, Image Vis. Comput..

[23]  DurouJean-Denis,et al.  Numerical methods for shape-from-shading , 2008 .

[24]  Edwin R. Hancock,et al.  A graph-spectral method for surface height recovery from needle-maps , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[25]  Reinhard Klette,et al.  Height data from gradient fields , 1996 .