Pose estimation from reflections for specular surface recovery

This paper addresses the problem of estimating the poses of a reference plane in specular shape recovery. Unlike existing methods which require an extra mirror or an extra reference plane and camera, our proposed method recovers the poses of the reference plane directly from its reflections on the specular surface. By establishing reflection correspondences on the reference plane in three distinct poses, our method estimates the poses of the reference plane in two steps. First, by applying a colinearity constraint to the reflection correspondences, a simple closed-form solution is derived for recovering the poses of the reference plane relative to its initial pose. Second, by applying a ray incidence constraint to the incident rays formed by the reflection correspondences and the visual rays cast from the image, a closed-form solution is derived for recovering the poses of the reference plane relative to the camera. The shape of the specular surface then follows. Experimental results on both synthetic and real data are presented, which demonstrate the feasibility and accuracy of our proposed method.

[1]  P. Perona,et al.  Local analysis for 3D reconstruction of specular surfaces , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[2]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[3]  Richard Szeliski,et al.  High-accuracy stereo depth maps using structured light , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[4]  Andrew Blake,et al.  The information available to a moving observer from specularities , 1989, Image Vis. Comput..

[5]  Andrew Blake,et al.  Geometry From Specularities , 1988, [1988 Proceedings] Second International Conference on Computer Vision.

[6]  Zhengyou Zhang,et al.  A Flexible New Technique for Camera Calibration , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Kiriakos N. Kutulakos,et al.  A Theory of Refractive and Specular 3D Shape by Light-Path Triangulation , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[8]  A. Torralba,et al.  Specular reflections and the perception of shape. , 2004, Journal of vision.

[9]  Peter F. Sturm,et al.  How to Compute the Pose of an Object Without a Direct View? , 2006, ACCV.

[10]  Shree K. Nayar,et al.  A Theory of Specular Surface Geometry , 2004, International Journal of Computer Vision.

[11]  Tim Weyrich,et al.  Dense 3D reconstruction from specularity consistency , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[12]  Pietro Perona,et al.  Local Analysis for 3D Reconstruction of Specular Surfaces - Part II , 2002, ECCV.

[13]  Jean-Yves Bouguet,et al.  Camera calibration toolbox for matlab , 2001 .

[14]  Pau Gargallo,et al.  General Specular Surface Triangulation , 2006, ACCV.

[15]  Charles R. Johnson,et al.  Topics in Matrix Analysis , 1991 .

[16]  Ohad Ben-Shahar,et al.  A linear formulation of shape from specular flow , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[17]  Zhenwen Dai,et al.  Specular Surface Recovery from Reflections of a Planar Pattern Undergoing an Unknown Pure Translation , 2010, ACCV.

[18]  Aswin C. Sankaranarayanan,et al.  Specular surface reconstruction from sparse reflection correspondences , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[19]  Ohad Ben-Shahar,et al.  Toward a Theory of Shape from Specular Flow , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[20]  Michael J. Black,et al.  Specular Flow and the Recovery of Surface Structure , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[21]  Peter F. Sturm,et al.  Voxel carving for specular surfaces , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[22]  Min Chen,et al.  Theory and application of specular path perturbation , 2000, TOGS.