Asymmetric post-processing for stereo correspondence

This paper presents a novel approach that performs post-processing for stereo correspondence. We improve the performance of stereo correspondence by performing consistency check and adaptive filtering in an iterative filtering scheme. The consistency check is done with asymmetric information only so that very few additional computational loads are necessary. The proposed post-filtering method can be used in various methods for stereo correspondence without any modification. We demonstrate the validity of the proposed method by applying it to hierarchical belief propagation and semi-global matching.

[1]  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).

[2]  Marc Pollefeys,et al.  Temporally Consistent Reconstruction from Multiple Video Streams Using Enhanced Belief Propagation , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[3]  Andreas Klaus,et al.  Segment-Based Stereo Matching Using Belief Propagation and a Self-Adapting Dissimilarity Measure , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[4]  Daniel P. Huttenlocher,et al.  Efficient Belief Propagation for Early Vision , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[5]  Richard Szeliski,et al.  Handling occlusions in dense multi-view stereo , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[6]  Roberto Manduchi,et al.  Bilateral filtering for gray and color images , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

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

[8]  Federico Tombari,et al.  Stereo Vision Enabling Precise Border Localization Within a Scanline Optimization Framework , 2007, ACCV.

[9]  D. Nistér,et al.  Stereo Matching with Color-Weighted Correlation, Hierarchical Belief Propagation, and Occlusion Handling , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Heiko Hirschmüller,et al.  Stereo Vision in Structured Environments by Consistent Semi-Global Matching , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[11]  Sing Bing Kang,et al.  Stereo for Image-Based Rendering using Image Over-Segmentation , 2007, International Journal of Computer Vision.

[12]  Ruigang Yang,et al.  Stereo Matching with Color-Weighted Correlation, Hierarchical Belief Propagation and Occlusion Handling , 2006, CVPR.