[1] Chen Y, Yang L L, Wang Z P. Literature survey on stereo vision matching algorithms[J]. Journal of Graphics, 41, 702-708(2020).

[2] Žbontar J, LeCun Y. Computing the stereo matching cost with a convolutional neural network[C], 1592-1599(2015).

[3] Mayer N, Ilg E, Häusser P et al. A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation[C], 4040-4048(2016).

[4] Kendall A, Martirosyan H, Dasgupta S et al. End-to-end learning of geometry and context for deep stereo regression[C], 66-75(2017).

[5] Chang J R, Chen Y S. Pyramid stereo matching network[C], 5410-5418(2018).

[6] Liu J G, Ji G, Yan F W et al. Stereo matching network based on disparity optimization[J]. Computer Engineering, 48, 220-228(2022).

[7] Wang Y F, Wang H W, Liu Y et al. Algorithm for stereo matching based on multi-task learning[J]. Laser & Optoelectronics Progress, 58, 0415010(2021).

[8] Wang Y F, Wang H W, Liu Y et al. Real-time stereo matching algorithm with hierarchical refinement[J]. Acta Optica Sinica, 40, 0915002(2020).

[9] Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C], 7132-7141(2018).

[10] Woo S, Park J, Lee J Y et al. CBAM: convolutional block attention module[M]. Ferrari V, Hebert M, Sminchisescu C, et al, Computer vision-ECCV 2018. Lecture notes in computer science, 11211, 3-19(2018).

[11] Wang X L, Girshick R, Gupta A et al. Non-local neural networks[C], 7794-7803(2018).

[12] Cheng M Y, Gai S Y, Da F P. A stereo-matching neural network based on attention mechanism[J]. Acta Optica Sinica, 40, 1415001(2020).

[13] Zhang Y R, Kong Y T, Liu B. Multi dimensional attention feature aggregation stereo matching algorithm[J/OL]. Acta Automatica Sinica, 1-12. https://doi.org/10.16383/j.aas.c200778

[14] Zhang W, Shao X T, Yang W et al. An efficient and accurate stereo matching algorithm based on convolutional neural network[J]. Journal of Computer-Aided Design & Computer Graphics, 32, 45-53(2020).

[15] Huang J H, Zhang R F, Liu Y H et al. Optimized deep learning stereo matching algorithm[J]. Laser & Optoelectronics Progress, 58, 2433002(2021).

[16] Gong W, Qin L, Ren G F et al. Binocular stereo matching algorithm based on multidimensional feature fusion[J]. Laser & Optoelectronics Progress, 57, 161501(2020).

[17] Hou Q B, Zhou D Q, Feng J S. Coordinate attention for efficient mobile network design[C], 13708-13717(2021).

[18] Vaswani A, Shazeer N, Parmar N et al. Attention is all you need[EB/OL]. https://arxiv.org/abs/1706.03762v3

[19] Huang Z L, Wang X G, Huang L C et al. CCNet: criss-cross attention for semantic segmentation[C], 19398730(2019).

[20] Shen Z L, Dai Y C, Rao Z B. MSMD-net: deep stereo matching with multi-scale and multi-dimension cost volume[EB/OL]. https://arxiv.org/abs/2006.12797

[21] Misra D. Mish: a self regularized non-monotonic neural activation function[EB/OL]. https://arxiv.org/abs/1908.08681v2

[22] Yang G R, Zhao H S, Shi J P et al. SegStereo: exploiting semantic information for disparity estimation[M]. Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science, 11211, 660-676(2018).

[23] Badki A, Troccoli A, Kim K et al. Bi3D: stereo depth estimation via binary classifications[C], 1597-1605(2020).

[24] Duggal S, Wang S L, Ma W C et al. DeepPruner: learning efficient stereo matching via differentiable PatchMatch[C], 4383-4392(2019).

[25] Guo X Y, Yang K, Yang W K et al. Group-wise correlation stereo network[C], 3268-3277(2019).

[26] Wu Z Y, Wu X Y, Zhang X P et al. Semantic stereo matching with pyramid cost volumes[C], 7483-7492(2019).