[1] Mayer N, Ilg E, Hausser P et al. A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation. [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, LasVegas, NV, USA. New York: IEEE, 4040-4048(2016).

[2] Kendall A, Martirosyan H, Dasgupta S et al. End-to-end learning of geometry and context for deep stereo regression. [C]∥2017 IEEE International Conference on Computer Vision (ICCV), October 22-29, 2017, Venice, Italy. New York: IEEE, 66-75(2017).

[3] Chang J R, Chen Y S. Pyramid stereo matching network. [C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA. New York: IEEE, 5410-5418(2018).

[4] 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).

[5] Wang Q, Piao Y. Depth image acquisition technology based on improved genetic algorithm[J]. Laser & Optoelectronics Progress, 55, 021003(2018).

[6] Zhang Y, Khamis S, Rhemann C et al. Active Stereo Net: end-to-end self-supervised learning for active stereo systems. [C]∥Proceedings of the European Conference on Computer Vision (ECCV), September 8-14, 2018, Munich, Germany. New York: IEEE, 784-801(2018).

[7] Žbontar J. LeCun Y. Computing the stereo matching cost with a convolutional neural network. [C]∥Proceedings of the IEEE conference on computer vision and pattern recognition, June 7-12, 2015, Boston, USA. New York: IEEE, 1592-1599(2015).

[8] Xiao J S, Tian H, Zou W T et al. Stereo matching based on convolutional neural network[J]. Acta Optica Sinica, 38, 0815017(2018).

[9] Spyropoulos A, Komodakis N, Mordohai P. Learning to detect ground control points for improving the accuracy of stereo matching. [C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2014, Columbus, Ohio. New York: IEEE, 1621-1628(2014).

[10] Taniai T, Matsushita Y, Naemura T. Graph cut based continuous stereo matching using locally shared labels. [C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2014, Columbus, Ohio. New York: IEEE, 1613-1620(2014).

[11] Sinha S N, Scharstein D, Szeliski R. Efficient high-resolution stereo matching using local plane sweeps. [C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2014, Columbus, Ohio. New York: IEEE, 1582-1589(2014).

[12] Zhang K, Fang Y, Min D et al. Cross-scale cost aggregation for stereo matching. [C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2014, Columbus, Ohio. New York: IEEE, 1590-1597(2014).

[13] Pang J, Sun W, Yang C et al. Zoom and learn: generalizing deep stereo matching to novel domains. [C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 18-22, 2018, Salt Lake City, Utah. New York: IEEE, 2070-2079(2018).

[14] Tonioni A, Tosi F, Poggi M et al. Real-time self-adaptive deep stereo. [C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 16-20, 2019, Long Beach, CA. New York: IEEE, 195-204(2019).

[15] Geiger A, Roser M, Urtasun R. Efficient large-scale stereo matching[M]. ∥Kimmel R, Klette R, Sugimoto A. Computer vision-ACCV 2010. Lecture notes in computer science. Heidelberg: Springer, 6492, 25-38(2010).

[16] Wu P L, Li Y N, Yang F et al. A CLM-based method of indoor affordance areas classification for service robots[J]. Robot, 40, 188-194(2018).

[17] Wu S, Xu J Z, Zhang Y X et al. Reliability evaluation method and application for light-stripe-center extraction[J]. Acta Optica Sinica, 31, 1115001(2011).

[18] Scharstein D, Szeliski R. -10-15)[2019-08-26]. http:∥vision.middlebury.edu/stereo/.(2014).

[19] Guo X, Yang K, Yang W et al. Group-wise correlation stereo network. [C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 16-20, 2019, Long Beach, CA. New York: IEEE, 3273-3282(2019).

[20] Zhang F, Prisacariu V, Yang R et al. GA-Net: guided aggregation net for end-to-end stereo matching. [C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 16-20, 2019, Long Beach, CA. New York: IEEE, 185-194(2019).