Stereo Matching with Confidence-Region Decomposition and Processing

AbstractWe introduce a new stereo matching algorithm that estimates disparities in high-confidence and low-confidence regions separately . Stereo matching algorithm play an important role in 3D rendering since 3D structures and virtual scenes can be built by disparity map. A complementary tree structure is adopted to identify the high-confidence region and estimate its disparity map using dynamic programming. Then, a disparity fitting algorithm restores the disparities in low-confidence regions using the color and disparity information of high-confidence regions through a global optimization technique. The proposed stereo matching algorithm enhances disparity values in both occlusion and difficult-to-estimate areas (e.g., thin objects), to yield a high quality disparity map.

[1]  Richard Szeliski,et al.  A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms , 2001, International Journal of Computer Vision.

[2]  Olga Veksler,et al.  Stereo correspondence by dynamic programming on a tree , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[3]  Neil A. Dodgson,et al.  Autostereoscopic 3D displays , 2005, Computer.

[4]  Stan Z. Li,et al.  Markov Random Field Modeling in Image Analysis , 2001, Computer Science Workbench.

[5]  Ji-Sang Yoo,et al.  Disparity Refinement near the Object Boundaries for Virtual-View Quality Enhancement , 2015 .

[6]  Margrit Gelautz,et al.  Simple but Effective Tree Structures for Dynamic Programming-Based Stereo Matching , 2008, VISAPP.

[7]  Andrew Blake,et al.  Efficient Dense Stereo with Occlusions for New View-Synthesis by Four-State Dynamic Programming , 2006, International Journal of Computer Vision.

[8]  Ingemar J. Cox,et al.  A Maximum Likelihood Stereo Algorithm , 1996, Comput. Vis. Image Underst..

[9]  Dani Lischinski,et al.  A Closed-Form Solution to Natural Image Matting , 2008 .

[10]  Ji-Sang Yoo,et al.  3D Conversion of 2D Video Encoded by H.264 , 2012 .

[11]  Ji-Sang Yoo,et al.  Real-time Virtual-viewpoint Image Synthesis Algorithm Using Kinect Camera , 2014 .

[12]  R. Zabih,et al.  Efficient Graph-Based Energy Minimization Methods in Computer Vision , 1999 .

[13]  Olga Veksler,et al.  Fast Approximate Energy Minimization via Graph Cuts , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  H. Hirschmüller Accurate and Efficient Stereo Processing by Semi-Global Matching and Mutual Information , 2005, CVPR.

[15]  Jayant Shah A nonlinear diffusion model for discontinuous disparity and half-occlusions in stereo , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[16]  Vladimir Kolmogorov,et al.  Computing visual correspondence with occlusions using graph cuts , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[17]  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..

[18]  Jian Sun,et al.  Symmetric stereo matching for occlusion handling , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[19]  Narendra Ahuja,et al.  A constant-space belief propagation algorithm for stereo matching , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[20]  Nanning Zheng,et al.  Stereo Matching Using Belief Propagation , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  Jiwon Kim,et al.  Confidence stereo matching using complementary tree structures and global depth-color fitting , 2013, 2013 IEEE International Conference on Consumer Electronics (ICCE).