[1] 王金鹤, 车志龙, 张楠, 等.基于多尺度和多层级特征融合的立体匹配算法[J].计算机工程, 2021, 47(3):243-248. WANG J H, CHE Z L, ZHANG N, et al.Stereo matching based on multi-scale and multi-feature integration[J].Computer Engineering, 2021, 47(3):243-248.(in Chinese) [2] 赵晨园, 李文新, 张庆熙.一种改进的实时半全局立体匹配算法及硬件实现[J].计算机工程, 2021, 47(9):162-170. ZHAO C Y, LI W X, ZHANG Q X.An improved real-time semi-global stereo matching algorithm and its hardware implementation[J].Computer Engineering, 2021, 47(9):162-170.(in Chinese) [3] 陈炎, 杨丽丽, 王振鹏.双目视觉的匹配算法综述[J].图学学报, 2020, 41(5):702-708. CHEN Y, YANG L L, WANG Z P.Literature survey on stereo vision matching algorithms[J].Journal of Graphics, 2020, 41(5):702-708.(in Chinese) [4] ZBONTAR J, LECUN Y.Computing the stereo matching cost with a convolutional neural network[C]//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2015:1592-1599. [5] ZHANG K, LU J B, LAFRUIT G.Cross-based local stereo matching using orthogonal integral images[J].IEEE Transactions on Circuits and Systems for Video Technology, 2009, 19(7):1073-1079. [6] HIRSCHMULLER H.Accurate and efficient stereo processing by semi-global matching and mutual information[C]//Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2005:807-814. [7] HIRSCHMULLER H.Stereo processing by semi-global matching and mutual information[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008, 30(2):328-341. [8] LUO W J, SCHWING A G, URTASUN R.Efficient deep learning for stereo matching[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2016:5695-5703. [9] MAYER N, ILG E, HAUSSER P, et al.A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2016:4040-4048. [10] KENDALL A, MARTIROSYAN H, DASGUPTA S, et al.End-to-end learning of geometry and context for deep stereo regression[C]//Proceedings of 2017 IEEE International Conference on Computer Vision.Washington D.C., USA:IEEE Press, 2017:66-75. [11] PANG J H, SUN W X, REN J, et al.Cascade residual learning:a two-stage convolutional neural network for stereo matching[C]//Proceedings of 2017 IEEE International Conference on Computer Vision Workshops Venice.Washington D.C., USA:IEEE Press, 2017:878-886. [12] CHANG J R, CHEN Y S.Pyramid stereo matching network[C]//Proceedings of 2018 IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2018:5410-5418. [13] HE K M, ZHANG X Y, REN S Q, et al.Spatial pyramid pooling in deep convolutional networks for visual recognition[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 37(9):1904-1916. [14] ZHANG F H, PRISACARIU V, YANG R G, et al.GA-Net:guided aggregation net for end-to-end stereo matching[C]//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2019:185-194. [15] MA W C, WANG S L, HU R, et al.Deep rigid instance scene flow[C]//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2019:3609-3617. [16] XU H F, ZHANG J Y.AANet:adaptive aggregation network for efficient stereo matching[C]//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2020:1956-1965. [17] ZHU Z D, HE M Y, DAI Y C, et al.Multi-scale cross-form pyramid network for stereo matching[C]//Proceedings of the 14th IEEE Conference on Industrial Electronics and Applications.Washington D.C., USA:IEEE Press, 2019:1789-1794. [18] ZHANG Y M, CHEN Y M, BAI X, et al.Adaptive unimodal cost volume filtering for deep stereo matching[EB/OL].[2021-01-02].https://www.researchgate.net/publication/335713171_Adaptive_Unimodal_Cost_Volume_Filtering_for_Deep_Stereo_Matching. [19] CHEN L C, PAPANDREOU G, KOKKINOS I, et al.DeepLab:semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(4):834-848. [20] LI X T, YOU A S, ZHU Z, et al.Semantic flow for fast and accurate scene parsing[EB/OL].[2021-01-02].https://www.researchgate.net/publication/339471607_Semantic_Flow_for_Fast_and_Accurate_Scene_Parsing. [21] MENZE M, GEIGER A.Object scene flow for autonomous vehicles[C]//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2015:3061-3070. |