[1] [1] Hochreiter S, Schmidhuber J. Long short-term memory[J].Neural Computation, 1997,9(8): 1735-1780.

[2] [2] Eigen D, Puhrsch C, Fergus R. Depth map prediction from a single image using a multi-scale deep network[C]. Montreal: 28th Annual Conference on Neural Information Processing Systems, 2014.

[3] [3] Li J, Klein R, Yao A. A two-streamed network for estimating fine-scaled depth maps from single RGB images[C]. Venice: 2017 IEEE International Conference on Computer Vision, 2017.

[4] [4] Chen W, Fu Z, Yang D, et al. Single-image depth perception in the wild[C]. Las Vegas: 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016.

[5] [5] Lee J, Kim C S. Monocular depth estimation using relative depth maps[C]. Long Beach: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019.

[6] [6] Kumar A C, Bhandarkar S M, Prasad M. DepthNet: A recurrent neural network architecture for monocular depth prediction[C]. Salt Lake City: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018.

[7] [7] 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.

[8] [8] Jung H, Kim Y, Min D, et al. Depth prediction from a single image with conditional adversarial networks[C]. Beijing: 2017 IEEE International Conference on Image Processing, 2017.

[9] [9] Garg R, BG V K, Carneiro G, et al. Unsupervised CNN for single view depth estimation: geometry to the rescue[C]. Amsterdam: 14th European Conference on Computer Vision, 2016.

[10] [10] Godard C, Aodha O M, Brostow G J. Unsupervised monocular depth estimation with left-right consistency[C]. Honolulu: 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017.

[11] [11] Goldman M, Hassner T, Avidan S, et al. Learn stereo, infer mono: Siamese networks for self-supervised, monocular, depth estimation[C]. Long Beach: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019.

[12] [12] Andraghetti L, Myriokefalitakis P, Dovesi P L, et al. Enhancing self-supervised monocular depth estimation with traditional visual odometry[C]. Quebec City: 2019 International Conference on 3D Vision, 2019.

[13] [13] Watson J, Firman M, Brostow G, et al. Self-supervised monocular depth hints[C]. Seoul: 2019 IEEE/CVF International Conference on Computer Vision, 2019.

[14] [14] Rehman S U, Tu S, Waqas M, et al. Unsupervised pre-trained filter learning approach for efficient convolution neural network[J].Neurocomputing, 2019,365: 171-190.

[15] [15] Chang J R, Chen Y S. Pyramid stereo matching network[C]. Salt Lake City: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018.

[17] [17] Zhou T, Brown M, Snavely N, et al. Unsupervised learning of depth and ego-motion from video[C]. Honolulu: 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017.

[18] [18] Zheng C, Cham T J, Cai J. T2net: synthetic-to-realistic translation for solving single-image depth estimation tasks[C]. Munich: 15th European Conference on Computer Vision, 2018.

[19] [19] Kuznietsov Y, Stuckler J, Leibe B. Semi-supervised deep learning for monocular depth map prediction[C]. Honolulu: 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017.

[20] [20] Qi X, Liao R, Liu Z, et al. Geonet: Geometric neural network for joint depth and surface normal estimation[C]. Salt Lake City: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018.