Non-Local Stereo Matching Algorithm Based on Edge Constraint Iteration

Ying Luo; Guanying Huo; Jinxin Xu; Qingwu Li

doi:10.3788/LOP56.151501

[1] Jiang X D, Yu J Y, Zhu L K et al. Self-calibrated binocular ranging system based on hardware SURF algorithm[J]. Acta Optica Sinica, 38, 1036001(2018).

[2] Li D H, Shen H Y, Yu X et al. Binocular ranging method using stereo matching based on improved Census transform[J]. Laser & Optoelectronics Progress, 56, 111503(2019).

[3] Wang Q L, Li J Y, Shen H K. Target tracking system of binocular vision and laser range sensor[J]. Acta Optica Sinica, 36, 0912002(2016).

[4] Wang S, Xu X. 3D reconstruction based on horopter[J]. Acta Optica Sinica, 37, 0515004(2017).

[5] Scharstein D, Szeliski R. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms[J]. International Journal of Computer Vision, 47, 7-42(2002). http://portal.acm.org/citation.cfm?id=598475

[6] Wang K, Li Z W, Zhu C D et al. Local stereo matching algorithm based on secondary guided filtering[J]. Laser & Optoelectronics Progress, 56, 081004(2019).

[7] Yan L, Wang R, Liu H et al. Stereo matching method based on improved cost computation and adaptive guided filter[J]. Acta Optica Sinica, 38, 1115007(2018).

[8] Yoon K J, Kweon I S. Adaptive support-weight approach for correspondence search[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28, 650-656(2006). http://ieeexplore.ieee.org/document/1597121

[9] Su X, Chen X D, Xu H Y et al. Adaptive window local matching algorithm based on HSV color space[J]. Laser & Optoelectronics Progress, 55, 031103(2018).

[10] Sun J, Zheng N N, Shum H Y. Stereo matching using belief propagation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25, 787-800(2003). http://dl.acm.org/citation.cfm?id=649283

[11] Klaus A, Sormann M, Karner K. Segment-based stereo matching using belief propagation and a self-adapting dissimilarity measure. [C]∥18th International Conference on Pattern Recognition (ICPR'06), August 20-24, 2006, Hong Kong, China. New York: IEEE, 9209946(2006).

[12] Boykov Y, Veksler O, Zabih R. Fast approximate energy minimization via graph cuts. [C]∥Proceedings of the Seventh IEEE International Conference on Computer Vision, September 20-27, 1999, Kerkyra, Greece. New York: IEEE, 6365236(1999).

[13] Wang H Q, Wu M, Zhang Y B et al. Effective stereo matching using reliable points based graph cut. [C]∥2013 Visual Communications and Image Processing (VCIP), November 17-20, 2013, Kuching, Malaysia. New York: IEEE, 14028712(2013).

[14] Zhang S H, He H, Kong L F. Fusing multi-feature for video occlusion region detection based on graph cut[J]. Acta Optica Sinica, 35, 0415001(2015).

[15] Bleyer M, Gelautz M. Simple but effective tree structures for dynamic programming-based stereo matching. [C]∥Proceedings of the Third International Conference on Computer Vision Theory and Applications, January 22-25, 2008, Funchal, Madeira, Portugal. [S.l.]: [s.n.], 415-422(2008).

[16] Salehian B, Fotouhi A M, Raie A A. Dynamic programming-based dense stereo matching improvement using an efficient search space reduction technique[J]. Optik, 160, 1-12(2018). http://www.sciencedirect.com/science/article/pii/S0030402618300238

[17] Zhu S P, Yan L N, Li Z. Stereo matching algorithm based on improved Census transform and dynamic programming[J]. Acta Optica Sinica, 36, 0415001(2016).

[18] Mei X, Sun X, Zhou M C et al. On building an accurate stereo matching system on graphics hardware. [C]∥2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), November 6-13, 2011, Barcelona, Spain. New York: IEEE, 467-474(2011).

[19] Xu J X, Li Q W, Liu Y et al. Stereo matching algorithm based on color weights and tree dynamic programming[J]. Acta Optica Sinica, 37, 1215007(2017).

[20] Yang Q X. Stereo matching using tree filtering[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37, 834-846(2015). http://doi.ieeecomputersociety.org/10.1109/TPAMI.2014.2353642

[21] Mei X, Sun X, Dong W M et al. Segment-tree based cost aggregation for stereo matching. [C]∥2013 IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2013, Portland, OR, USA. New York: IEEE, 313-320(2013).

[22] Zhang K, Fang Y Q, Min D B et al. Cross-scale cost aggregation for stereo matching. [C]∥2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 23-28, 2014, Columbus, OH, USA. New York: IEEE, 1590-1597(2014).

[23] Yao P, Zhang H, Xue Y B et al. Iterative color-depth MST cost aggregation for stereo matching. [C]∥2016 IEEE International Conference on Multimedia and Expo (ICME), July 11-15, 2016, Seattle, WA, USA. New York: IEEE, 16266824(2016).

[24] Gao S Y, Ge H H, Zhang H et al. A nonlocal method with modified initial cost and multiple weight for stereo matching[J]. Journal of Sensors, 2017, 9374870(2017).

[25] Gerrits M, Bekaert P. Local stereo matching with segmentation-based outlier rejection. [C]∥The 3rd Canadian Conference on Computer and Robot Vision (CRV'06), June 7-9, 2006, Quebec, Canada. New York: IEEE(2006).

[26] Huang X M, Zhang Y J. An O(1) disparity refinement method for stereo matching[J]. Pattern Recognition, 55, 198-206(2016). http://www.sciencedirect.com/science/article/pii/S0031320316000467

[27] Yang Q Q. Local smoothness enforced cost volume regularization for fast stereo correspondence[J]. IEEE Signal Processing Letters, 22, 1429-1433(2015). http://ieeexplore.ieee.org/document/7055282/