A Linear Trinocular Rectification Method for Accurate Stereoscopic Matching

In this paper we propose and study a simple trinocular rectification method in which stratification to projective and affine components gives the rectifying homographies in a closed form. The class of trinocular rectifications which has 6 DOF is parametrized by an independent set of parameters with a geometric meaning. This offers the possibility to minimize rectification distortion in a natural way. It is shown experimentally on real data that our algorithm performs the rectification task correctly. As shown on groundtruth data using Confidently Stable Matching, trinocular matching significantly improves disparity map density and mismatch error, both depending on texture strength. Matching results on real complex scenes are reported.

[1]  Radim Sára,et al.  Finding the Largest Unambiguous Component of Stereo Matching , 2002, ECCV.

[2]  Ingemar J. Cox,et al.  Cylindrical rectification to minimize epipolar distortion , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[3]  Michael F. Cohen,et al.  Minimal Surfaces for Stereo , 2002, ECCV.

[4]  Shree K. Nayar,et al.  Rectifying transformations that minimize resampling effects , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[5]  Charles V. Stewart,et al.  The Trinocular General Support Algorithm: A Three-camera Stereo Algorithm For Overcoming Binocular Matching Errors , 1988, [1988 Proceedings] Second International Conference on Computer Vision.

[6]  Hans P. Morevec.  Towards automatic visual obstacle avoidance , 1977, IJCAI 1977.

[7]  Reinhard Koch,et al.  A simple and efficient rectification method for general motion , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[8]  Sigma-Delta Stable Matching for Computational Stereopsis ( Version 1 . 5 ) Radim Šára , 2001 .

[9]  Charles T. Loop,et al.  Computing rectifying homographies for stereo vision , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[10]  Hans P. Moravec.  Towards Automatic Visual Obstacle Avoidance , 1977, IJCAI.

[11]  Changming Sun,et al.  Uncalibrated three-view image rectification , 2003, Image Vis. Comput..

[12]  Richard I. Hartley,et al.  Theory and Practice of Projective Rectification , 1999, International Journal of Computer Vision.

[13]  Charles Hansen,et al.  Rectification of images for binocular and trinocular stereovision , 1988, [1988 Proceedings] 9th International Conference on Pattern Recognition.

[14]  Radim Sára.  Accurate Natural Surface Reconstruction from Polynocular Stereo , 2000, Confluence of Computer Vision and Computer Graphics.

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

[16]  Rachid Deriche,et al.  A Robust Technique for Matching two Uncalibrated Images Through the Recovery of the Unknown Epipolar Geometry , 1995, Artif. Intell..