[1] Wu W H, Zhu H, Yu S Y, et al. Stereo matching with fusing adaptive support weights[J]. IEEE Access, 2019, 7: 61960-61974.

[2] Scharstein D, Szeliski R. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms[J]. International Journal of Computer Vision, 2002, 47(1/2/3): 7-42.

[3] KolmogorovV, ZabihR. Computing visual correspondence with occlusionsusing graph cuts[C]∥ Proceedings of 8th IEEE International Conference on Computer Vision, July 7-14, 2001, Vancouver, BC, Canada.New York: IEEE Press, 2001: 508- 515.

[4] Besse F, Rother C, Fitzgibbon A, et al. PMBP:PatchMatch belief propagation for correspondence field estimation[J]. International Journal of Computer Vision, 2014, 110(1): 2-13.

[5] Birchfield S, Tomasi C. Depth discontinuities by pixel-to-pixel stereo[J]. International Journal of Computer Vision, 1999, 35(3): 269-293.

[6] ScharsteinD. Matching images by comparing their gradient fields[C]∥Proceedings of 12th International Conference on Pattern Recognition, October 9-13, 1994, Jerusalem, Israel.New York: IEEE Press, 1994: 572- 575.

[7] ZabihR, WoodfillJ. Non-parametric local transforms for computing visual correspondence[C]∥ Eklundh J O. Computer Vision: ECCV'94. Berlin, Heidelberg: Springer, 1994, 801: 151- 158.

[8] 陈先锋, 郭正华, 伍俊龙, 等. 基于区域先验信息的去遮挡立体匹配算法[J]. 激光与光电子学进展, 2019, 56(19): 191001.

    Chen X F, Guo Z H, Wu J L, et al. De-occlusion stereo matching algorithm based on regional prior information[J]. Laser & Optoelectronics Progress, 2019, 56(19): 191001.

[9] Hirschmuller H. Stereo processing by semiglobal matching and mutual information[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008, 30(2): 328-341.

[10] MeiX, SunX, Zhou MC, 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 Press, 2011: 467- 474.

[11] Hosni A, Rhemann C, Bleyer M, et al. Fast cost-volume filtering for visual correspondence and beyond[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(2): 504-511.

[12] 郭鑫, 王延杰, 付东辉, 等. 三种测度融合的加权引导滤波匹配算法[J]. 计算机工程与科学, 2019, 41(6): 1044-1049.

    Guo X, Wang Y J, Fu D H, et al. A weight guided filtering matching algorithm combining three measures[J]. Computer Engineering & Science, 2019, 41(6): 1044-1049.

[13] 孔令寅, 朱江平, 应三丛. 基于引导图像和自适应支持域的立体匹配[J]. 光学学报, 2020, 40(9): 0915001.

    Kong L Y, Zhu J P, Ying S C. Stereo matching based on guidance image and adaptive support region[J]. Acta Optica Sinica, 2020, 40(9): 0915001.

[14] 王云峰, 吴炜, 余小亮, 等. 基于自适应权重AD-Census变换的双目立体匹配[J]. 工程科学与技术, 2018, 50(4): 153-160.

    Wang Y F, Wu W, Yu X L, et al. A stereo matching system with the adaptive weight AD-census[J]. Advanced Engineering Sciences, 2018, 50(4): 153-160.

[15] 闫利, 王芮, 刘华, 等. 基于改进代价计算和自适应引导滤波的立体匹配[J]. 光学学报, 2018, 38(11): 1115007.

    Yan L, Wang R, Liu H, et al. Stereo matching method based on improved cost computation and adaptive guided filter[J]. Acta Optica Sinica, 2018, 38(11): 1115007.

[16] Salehian B, Raie A, Fotouhi A, et al. Efficient interscanline consistency enforcing method for dynamic programming-based dense stereo matching algorithms[J]. Journal of Electronic Imaging, 2013, 22(4): 043028.

[17] Wang L, Yang R G, Gong M L, et al. Real-time stereo using approximated joint bilateral filtering and dynamic programming[J]. Journal of Real-Time Image Processing, 2014, 9(3): 447-461.

[18] 彭新俊, 韩军, 汤踊, 等. 基于改进Census变换和异常值剔除的抗噪立体匹配算法[J]. 光学学报, 2017, 37(11): 1115004.

    Peng X J, Han J, Tang Y, et al. Anti-noise stereo matching algorithm based on improved census transform and outlier elimination[J]. Acta Optica Sinica, 2017, 37(11): 1115004.

[19] 彭建建, 白瑞林. 基于水平树结构的可变权重代价聚合立体匹配算法[J]. 光学学报, 2018, 38(1): 0115002.

    Peng J J, Bai R L. Variable weight cost aggregation algorithm for stereo matching based on horizontal tree structure[J]. Acta Optica Sinica, 2018, 38(1): 0115002.

[20] ZhangK, Fang YQ, Min DB, et al.Cross-scale cost aggregation for stereo matching[C]∥2014 IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2014, Columbus, OH, USA.New York: IEEE Press, 2014: 1590- 1597.

[21] 马晴晴, 王彩芳. 基于颜色和边缘信息的非局部立体匹配算法[J]. 激光与光电子学进展, 2020, 57(10): 101020.

    Ma Q Q, Wang C F. Non-local stereo matching algorithm based on color and edge information[J]. Laser & Optoelectronics Progress, 2020, 57(10): 101020.

[22] Zhu S P, Yan L N. Local stereo matching algorithm with efficient matching cost and adaptive guided image filter[J]. The Visual Computer, 2017, 33(9): 1087-1102.

[23] ScharsteinD, SzeliskiR. High-accuracy stereo depth maps using structured light[C]∥2003 IEEE Conference on Computer Vision and Pattern Recognition, June 18-20, 2003, Madison, WI, USA.New York: IEEE Press, 2003: 7762284.

[24] ScharsteinD, PalC. Learning conditional random fields for stereo[C]∥IEEE Conference on Computer Vision and Pattern Recognition, June 17-22, 2007, Minneapolis, MN, USA.New York: IEEE Press, 2007: 9738080.

[25] MattocciaS, TombariF, Stefano LD. Stereo vision enabling precise border localization within a scanline optimization framework[C]∥ Proceedings of Asian Conference on Computer Vision, Berlin/Heidelberg: Springer-Verlag, 2007: 517- 527.

[26] De-Maeztu L, Villanueva A, Cabeza R. Stereo matching using gradient similarity and locally adaptive support-weight[J]. Pattern Recognition Letters, 2011, 32(13): 1643-1651.

[27] 祝世平, 李政. 基于改进梯度和自适应窗口的立体匹配算法[J]. 光学学报, 2015, 35(1): 0110003.

    Zhu S P, Li Z. A stereo matching algorithm using improved gradient and adaptive window[J]. Acta Optica Sinica, 2015, 35(1): 0110003.