[1] 王芳, 陈超, 黄见曦. 无人驾驶汽车研究综述[J]. 中国水运, 2016, 16(12): 126-128.

    Wang F, Chen C, Huang J X. A review of research on driverless vehicles[J]. China Water Transport, 2016, 16(12): 126-128.

[2] SilverD, van HasseltH, HesselM, et al. The predictron: End-to-end learning and planning[EB/OL]. ( 2017-07-20)[2018-09-30]. org/abs/1612. 08810. https://arxiv.

[3] ScharsteinD, SzeliskiR, ZabihR. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms[C]∥Proceedings IEEE Workshop on Stereo and Multi-Baseline Vision (SMBV 2001), December 9-10, 2001, Kauai, HI, USA. New York: IEEE, 2001: 131- 140.

[4] FlynnJ, NeulanderI, PhilbinJ, et al. Deep stereo: Learning to predict new views from the world's imagery[C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 2016: 5515- 5524.

[5] SaxenaA, Chung SH, Ng AY. Learning depth from single monocular images[C]∥Conference and Workshop on Neural Information Processing Systems.[S.l.: s.n.].2005, 18: 1161- 1168.

[6] Saxena A, Sun M, Ng A Y. Make3D: learning 3D scene structure from a single still image[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(5): 824-840.

[7] Hoiem D, Efros A A, Hebert M. Recovering surface layout from an image[J]. International Journal of Computer Vision, 2007, 75(1): 151-172.

[8] LadickyL, Shi JB, PollefeysM. Pulling things out of perspective[C]∥2014 IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2014, Columbus, OH, USA. New York: IEEE, 2014: 89- 96.

[9] Choi S, Min D B, Ham B. et al. Depth analogy: data-driven approach for single image depth estimation using gradient samples[J]. IEEE Transactions on Image Processing, 2015, 24(12): 5953-5966.

[10] Konrad J, Wang M, Ishwar P. et al. Learning-based, automatic 2D-to-3D image and video conversion[J]. IEEE Transactions on Image Processing, 2013, 22(9): 3485-3496.

[11] Baig MH, TorresaniL. Coupled depth learning[C]∥2016 IEEE Winter Conference on Applications of Computer Vision (WACV), March 7-10, 2016, Lake Placid, NY, USA. New York: IEEE, 2016: 1- 10.

[12] Shi J P, Tao X, Xu L. et al. Break Ames room illusion[J]. ACM Transactions on Graphics, 2015, 34(6): 1-11.

[13] RanftlR, VineetV, Chen QF, et al. Dense monocular depth estimation in complex dynamic scenes[C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 2016: 4058- 4066.

[14] FurukawaR, SagawaR, KawasakiH. Depth estimation using structured light flow: Analysis of projected pattern flow on an Object's surface[C]∥2017 IEEE International Conference on Computer Vision (ICCV), October 22-29, 2017, Venice, Italy. New York: IEEE, 2017: 4650- 4658.

[15] HäneC, LadickyL, PollefeysM. Direction matters: Depth estimation with a surface normal classifier[C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA. New York: IEEE, 2015: 381- 389.

[16] You X G, Li Q, Tao D C. et al. Local metric learning for exemplar-based object detection[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2014, 24(8): 1265-1276.

[17] ZhuoW, SalzmannM, He XM, et al. Indoor scene structure analysis for single image depth estimation[C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA. New York: IEEE, 2015: 614- 622.

[18] Liu MM, SalzmannM, He XM. Discrete-continuous depth estimation from a single image[C]∥2014 IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2014, Columbus, OH, USA. New York: IEEE, 2014: 716- 723.

[19] Karsch K, Liu C, Kang S B. Depth transfer: depth extraction from video using non-parametric sampling[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(11): 2144-2158.

[20] Oliva A, Torralba A. Modeling the shape of the scene: A holistic representation of the spatial envelope[J]. International Journal of Computer Vision, 2001, 42(3): 145-175.

[21] SimonyanK, Zisserman A. Very deep convolutional networks for large-scale image recognition[EB/OL].( 2015-04-10)[2018-09-30]. org/abs/1409. 1556. https://arxiv.

[22] He KM, Zhang XY, Ren SQ, et al. Deep residual learning for image recognition[C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 2016: 770- 778.

[23] 许路, 赵海涛, 孙韶媛. 基于深层卷积神经网络的单目红外图像深度估计[J]. 光学学报, 2016, 36(7): 0715002.

    Xu L, Zhao H T, Sun S Y. The Predictron: End-to-end learning and planning[J]. Acta Optica Sinica, 2016, 36(7): 0715002.

[24] EigenD, PuhrschC, Fergus R. Depth map prediction from a single image using a multi-scale deep network[EB/OL].( 2014-06-09)[2018-09-30]. https://arxiv.org/pdf/1406.2283v1.pdf.

[25] EigenD, FergusR. Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture[C]∥2015 IEEE International Conference on Computer Vision (ICCV), December 7-13, 2015, Santiago, Chile. New York: IEEE, 2015: 2650- 2658.

[26] Xie JY, GirshickR, FarhadiA. Deep3D: Fully Automatic 2D-to-3D Video Conversion with Deep Convolutional Neural Networks[M] ∥Xie J Y, Girshick R, Farhadi A. eds. Computer Vision-ECCV 2016. Cham: Springer International Publishing, 2016: 842- 857.

[27] 李素梅, 雷国庆, 范如. 基于卷积神经网络的深度图超分辨率重建[J]. 光学学报, 2017, 37(12): 1210002.

    Li S M, Lei G Q, Fan R. Depth map super-resolution reconstruction based on convolutional neural networks[J]. Acta Optica Sinica, 2017, 37(12): 1210002.

[28] 吴寿川, 赵海涛, 孙韶媛. 基于双向递归卷积神经网络的单目红外视频深度估计[J]. 光学学报, 2017, 37(12): 1215003.

    Wu S C, Zhao H T, Sun S Y. Depth estimation from monocular infrared video based on Bi-recursive convolutional neural network[J]. Acta Optica Sinica, 2017, 37(12): 1215003.

[29] 徐冉, 张俊格, 黄凯奇. 利用双通道卷积神经网络的图像超分辨率算法[J]. 中国图象图形学报, 2016, 21(5): 556-564.

    Xu R, Zhang J G, Huang K Q. Image super-resolution using two-channel convolutional neural networks[J]. Journal of Image and Graphics, 2016, 21(5): 556-564.

[30] Liu F Y, Shen C H, Lin G S. et al. Learning depth from single monocular images using deep convolutional neural fields[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(10): 2024-2039.

[31] LiB, Shen CH, Dai YC, et al. Depth and surface normal estimation from monocular images using regression on deep features and hierarchical CRFs[C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA. New York: IEEE, 2015: 1119- 1127.

[32] WangP, Shen XH, LinZ, et al. Towards unified depth and semantic prediction from a single image[C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA. New York: IEEE, 2015: 2800- 2809.

[33] Li B, Dai Y C, He M Y. Monocular depth estimation with hierarchical fusion of dilated CNNs and soft-weighted-sum inference[J]. Pattern Recognition, 2018, 83: 328-339.

[34] LiJ, KleinR, YaoA. A two-streamed network for estimating fine-scaled depth maps from single RGB images[C]∥2017 IEEE International Conference on Computer Vision (ICCV), October 22-29, 2017, Venice, Italy. New York: IEEE, 2017: 3392- 3400.

[35] Lee JH, HeoM, Kim KR, et al. Single-image depth estimation based on Fourier domain analysis[C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA. New York: IEEE, 2018: 330- 339.

[36] UmmenhoferB, Zhou HZ, UhrigJ, et al. DeMoN: depth and motion network for learning monocular stereo[C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 2017: 5622- 5631.

[37] FuH, Gong MM, Wang CH, et al. Deep ordinal regression network for monocular depth estimation[C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA. New York: IEEE, 2018: 2002- 2011.

[38] JégouS, DrozdzalM, VazquezD, et al. The one hundred layers tiramisu: fully convolutional DenseNets for semantic segmentation[C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 2017: 1175- 1183.

[39] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60(6): 84-90.

[40] Kingma DP, Ba J. Adam: A method for stochastic optimization[EB/OL]. ( 2017-01-30)[2018-09-30]. org/abs/1412. 6980. https://arxiv.

[41] LainaI, RupprechtC, BelagiannisV, et al. Deeper depth prediction with fully convolutional residual networks[C]∥2016 Fourth International Conference on 3D Vision (3DV), October 25-28, 2016, Stanford, CA, USA. New York: IEEE, 2016: 239- 248.

[42] Yin XC, Wang XW, Du XG, et al. Scale recovery for monocular visual odometry using depth estimated with deep convolutional neural fields[C]∥2017 IEEE International Conference on Computer Vision (ICCV), October 22-29, 2017, Venice, Italy. New York: IEEE, 2017: 5871- 5879.

[43] Dimitrievski M, Goossens B, Veelaert P. et al. High resolution depth reconstruction from monocular images and sparse point clouds using deep convolutional neural network[J]. Proceedings of SPIE, 2017, 10410: 104100H.

[44] Mancini M, Costante G, Valigi P. et al. Toward domain independence for learning-based monocular depth estimation[J]. IEEE Robotics and Automation Letters, 2017, 2(3): 1778-1785.