Evaluation of Cost Functions for Stereo Matching

Stereo correspondence methods rely on matching costs for computing the similarity of image locations. In this paper we evaluate the insensitivity of different matching costs with respect to radiometric variations of the input images. We consider both pixel-based and window-based variants and measure their performance in the presence of global intensity changes (e.g., due to gain and exposure differences), local intensity changes (e.g., due to vignetting, non-Lambertian surfaces, and varying lighting), and noise. Using existing stereo datasets with ground-truth disparities as well as six new datasets taken under controlled changes of exposure and lighting, we evaluate the different costs with a local, a semi-global, and a global stereo method.

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

[2]  Ruzena Bajcsy,et al.  On occluding contour artifacts in stereo vision , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[3]  Kurt Konolige,et al.  Small Vision Systems: Hardware and Implementation , 1998 .

[4]  Richard Szeliski,et al.  A Comparative Study of Energy Minimization Methods for Markov Random Fields , 2006, ECCV.

[5]  Olga Veksler,et al.  Fast approximate energy minimization via graph cuts , 2001, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[6]  Heiko Hirschmüller,et al.  Stereo vision based mapping and immediate virtual walkthroughs , 2003 .

[7]  Vladimir Kolmogorov,et al.  Visual correspondence using energy minimization and mutual information , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[8]  In-So Kweon,et al.  Adaptive Support-Weight Approach for Correspondence Search , 2006, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Jonathan M. Garibaldi,et al.  Real-Time Correlation-Based Stereo Vision with Reduced Border Errors , 2002, International Journal of Computer Vision.

[10]  Ruigang Yang,et al.  How Far Can We Go with Local Optimization in Real-Time Stereo Matching , 2006, Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06).

[11]  Richard Szeliski,et al.  High-quality video view interpolation using a layered representation , 2004, SIGGRAPH 2004.

[12]  Vladimir Kolmogorov,et al.  What energy functions can be minimized via graph cuts? , 2002, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Ramin Zabih,et al.  Non-parametric Local Transforms for Computing Visual Correspondence , 1994, ECCV.

[14]  Geoffrey Egnal,et al.  Mutual Information as a Stereo Correspondence Measure , 2000 .

[15]  Paul A. Viola,et al.  Alignment by Maximization of Mutual Information , 1997, International Journal of Computer Vision.

[16]  Ruigang Yang,et al.  BRDF Invariant Stereo Using Light Transport Constancy , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Shree K. Nayar,et al.  Motion estimation using ordinal measures , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[18]  Vladimir Kolmogorov,et al.  An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision , 2001, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Darius Burschka,et al.  Advances in Computational Stereo , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  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..

[21]  Carlo Tomasi,et al.  A Pixel Dissimilarity Measure That Is Insensitive to Image Sampling , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  D. Scharstein,et al.  A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms , 2001, Proceedings IEEE Workshop on Stereo and Multi-Baseline Vision (SMBV 2001).