Multi-Baseline Stereo Using Surface Extraction.

Abstract : Traditionally, the matching problem in stereo vision has been formulated as an ill-posed problem. However, it has been shown that the matching problem can be well-posed in scenes with no occlusions or depth discontinuities. Unfortunately most real scenes do not obey these constraints. We overcome this by finding regions within the images in which the matching problem is well-posed. That is, we find image regions in which there are no occlusions or depth discontinuities. In general, a unique set of such regions does not exist. However, we will demonstrate that in almost all cases these regions can be found efficiently. Therefore the matching problem can be well-posed in almost all cases. In order to find these corresponding regions we transform the problem from finding 2D image regions into identifying 3D surfaces. We have developed a method of 3D surface extraction which uniquely identifies correct 3D surfaces from a set of potential surfaces. In order to test the method we have built a four camera system with which we will present results from several scenes.

[1]  Tomaso Poggio,et al.  A Theory of Human Stereo Vision , 1977 .

[2]  Hans P. Moravec.  Obstacle avoidance and navigation in the real world by a seeing robot rover , 1980 .

[3]  Dana H. Ballard,et al.  Computer Vision , 1982 .

[4]  Martin A. Fischler,et al.  Computational Stereo , 1982, CSUR.

[5]  T. Poggio,et al.  Ill-Posed Problems and Regularization Analysis in Early Vision , 1984 .

[6]  Takeo Kanade,et al.  Stereo by Intra- and Inter-Scanline Search Using Dynamic Programming , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Berthold K. P. Horn.  Robot vision , 1986, MIT electrical engineering and computer science series.

[8]  M. Bertero,et al.  Ill-posed problems in early vision , 1988, Proc. IEEE.

[9]  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.

[10]  Gérard G. Medioni,et al.  Parallel Multiscale Stereo Matching Using Adaptive Smoothing , 1990, ECCV.

[11]  Juyang Weng,et al.  A theory of image matching , 1990, [1990] Proceedings Third International Conference on Computer Vision.

[12]  Takeo Kanade,et al.  A locally adaptive window for signal matching , 1990, [1990] Proceedings Third International Conference on Computer Vision.

[13]  T. Kanade,et al.  Experiments in Multiple-Baseline Stereo , 1992 .

[14]  Peter N. Belhumeur,et al.  A binocular stereo algorithm for reconstructing sloping, creased, and broken surfaces in the presence of half-occlusion , 1993, 1993 (4th) International Conference on Computer Vision.

[15]  Bill Ross,et al.  A practical stereo vision system , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[16]  Jon A. Webb.  Implementation and performance of fast parallel multi-baseline stereo vision , 1993, 1993 Computer Architectures for Machine Perception.

[17]  Takeo Kanade,et al.  A Multiple-Baseline Stereo , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  O. Faugeras.  Three-dimensional computer vision: a geometric viewpoint , 1993 .

[19]  A. Jepson,et al.  Recovering local surface structure through local phase difference measurements , 1994 .

[20]  Takeo Kanade,et al.  A Stereo Matching Algorithm with an Adaptive Window: Theory and Experiment , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  Takeo Kanade,et al.  A multibaseline stereo system with active illumination and real-time image acquisition , 1995, Proceedings of IEEE International Conference on Computer Vision.

[22]  Yalin Xiong.  High precision image matching and shape recovery , 1996 .

[23]  Mark W. Maimone,et al.  Characterizing Stereo Matching Problems using Local Spatial Frequency , 1996 .

[24]  Yuichi Ohta,et al.  Occlusion detectable stereo-occlusion patterns in camera matrix , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.