[1] [1] Cordts M, Omrran M, Ramos S, et al. The cityyscapes dataset foor semantic urban scene understandding[C]//IEEE Co mputer Vision annd Pattern Recogni tion, 2016: 3213-33223

[2] [2] PENG X, SUN B, Ali K, et al. LLearning deep objject detectors fromm 3D models[C]//IIEEE Computer VVision and Patternn Recognition, 20115: 1278-1286.

[3] [3] Biswas J, Veloos oM. Depth camera based inddoor mobile roboot localization and navigation[C]//IEEEE Robotics and Automation, 201 1: 1697-1702.

[4] [4] Sivaraman S, Tr ivedi M M. Combbining monocular and stereo-vision for real-time vehiclee ranging and traccking on multilanee highways[C]//IEEEE Intelligent Transsportation Systemss, 2011: 1249-12554.

[5] [5] Hedau V, Hoieem D, Forsyth DD. Thinking insside the box: ussing appearance models and context b ased on room geoometry[C]//Europeean Conference on CComputer Vision, 22010: 224-237.

[6] [6] Saxena A, SUNN M, Ng A Y. M ake3D: learning 3D scene structurre from a single stilll image[J]. IEEETransactions on Pattern Analysis & Machine Intelliggence, 2009, 31(5)): 824-840.

[7] [7] LIU B, Gould S, Koller D. Sinngle image depthh estimation fromm predicted semanntic labels[C]//IEEEE Computer VVision and Patterrn Recognition, 20110: 1253-1260.

[8] [8] Russell B C, Toorralba A. Buildinng a database of 3DD scenes from useer annotations[C]///IEEE Computerr Vision and Paattern Recognitionn, 2009: 2711-27118.

[9] [9] LIU M, Salzmaann M, HE X. D iscrete-continuou s depth estimatioon from a single image[C]//IEEEE Computer Vission and Patterrn Recognition, 20114: 716-723. 587.

[10] [10] Karsch K, LIU C, KANG S B. Depth extraction from video using non-parametric sampling[C]//European Conference on Computer Vision, 2012: 775-788.

[11] [11] Eigen D, Puhrsch C, Fergus R. Depth map prediction from a single image using a multi-scale deep network[C]//International Conference on Neural Information Processing Systems, 2014: 2366-2374.

[12] [12] Laina I, Rupprecht C, Belagiannis V, et al. Deeper depth prediction with fully convolutional residual networks[J]. International Conference on 3D Vision, 2016: 239-248.

[14] [14] WU S C, ZHAO H T, SUN S Y. Depth estimation from infrared video using local-feature-flow neural network[J/OL]. International Journal of Machine Learning and Cybernetics, 2018: doi.org/10.1007/s13042-018-0891-9.

[15] [15] HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//IEEE Computer Vision and Pattern Recognition, 2016: 770-778.

[16] [16] CHEN L C, Papandreou G, Kokkinos I, et al. Semantic image segmentation with deep convolutional nets and fully connected CRFs[J]. Computer Science, 2015(4): 357-361.

[17] [17] Kr?henbühl P, Koltun V. Efficient inference in fully connected CRFs with Gaussian edge potentials[J]. In Advances in Neural Information Processing Systems, 2012(24): 109-117.

[18] [18] LI N B, SHEN N C, DAI N Y, et al. Depth and surface normal estimation from monocular images using regression on deep features and hierarchical CRFs[C]//IEEE Computer Vision and Pattern Recognition, 2015: 1119-1127.

[19] [19] LIU F, SHEN C, LIN G. Deep convolutional neural fields for depth estimation from a single image[C]//IEEE Computer Vision and Pattern Recognition, 2015: 5162-5170.

[20] [20] LIU F, SHEN C, LIN G, et al. Learning depth from single monocular images using deep convolutional neural fields[C]//IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015: 5162-5170.

[21] [21] XU D, WANG W, TANG H, et al. Structured attention guided convolutional neural fields for monocular depth estimation[C]//IEEE Computer Vision and Pattern Recognition, 2018: 3917-3925.

[22] [22] Ibarra-Castanedo C, González D, Klein M, et al. Infrared image processing and data analysis[J]. Infrared Physics and Technology, 2004, 46(1-2): 75-83.

[25] [25] HUANG G, LIU Z, Laurens V D M, et al. Densely connected convolutional networks[C]//IEEE Conference on Computer Vision and Pattern Recognition, 2017: arXiv:1608.06993.

[26] [26] Noh H, HONG S, HAN B. Learning deconvolution network for semantic segmentation[C]//IEEE International Conference on Computer Vision, 2015: 1520-1528.

[27] [27] CAO Y, WU Z, SHEN C. Estimating depth from monocular images as classification using deep fully convolutional residual networks[J]. IEEE Transactions on Circuits and Systems for Video Technology,2017(99): 1-1.

[28] [28] FU H, GONG M,WANG C, et al. Deep ordinal regression network for monocular depth estimation[C]// IEEE Computer Vision and Pattern Recognition, 2018: 2002-2011.