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In self-supervised monocular depth estimation, the depth discontinuity and motion objects' artifacts are still challenging problems.
Desouza, G.N., Kak, A.C.: Vision for mobile robot navigation: a survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 24
2002
Earlier work this paper cites.
Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press (2004)
2004
Earlier work this paper cites.
Zhou Wang, Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing 13
2004
Earlier work this paper cites.
Saxena, A., Sun, M., Ng, A.Y.: Make3d: Learning 3d scene structure from a single still image. IEEE Transactions on Pattern Analysis and Machine Intelligence 31
2009
Earlier work this paper cites.
Silberman, N., Hoiem, D., Kohli, P., Fergus, R.: Indoor segmentation and support inference from RGBD images. In: ECCV. vol. 7576, pp. 746–760 (2012)
2012
Earlier work this paper cites.
Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: Vision meets robotics: The KITTI dataset. I. J. Robotics Res. 32
2013
Earlier work this paper cites.
Eigen, D., Puhrsch, C., Fergus, R.: Depth map prediction from a single image using a multi-scale deep network. In: NIPS. pp. 2366–2374 (2014)
2014
Earlier work this paper cites.
Chen, C., Seff, A., Kornhauser, A., Xiao, J.: Deepdriving: Learning affordance for direct perception in autonomous driving. In: ICCV. pp. 2722–2730 (2015)
2015
Earlier work this paper cites.
Eigen, D., Fergus, R.: Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture. In: 2015 IEEE International Conference on Computer Vision, ICCV 2015, Santiago, Chile, December 7-13, 2015. pp. 2650–2658 (2015)
2015
Earlier work this paper cites.
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) ICLR (2015)
2015
Earlier work this paper cites.
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M.S., Berg, A.C., Li, F.: Imagenet large scale visual recognition challenge. International Journal of Computer Vision 115
2015
Earlier work this paper cites.
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., Schiele, B.: The cityscapes dataset for semantic urban scene understanding. In: CVPR (2016)
2016
Earlier work this paper cites.
Garg, R., Kumar, B.G.V., Carneiro, G., Reid, I.D.: Unsupervised CNN for single view depth estimation: Geometry to the rescue. In: ECCV. vol. 9912, pp. 740–756 (2016)
2016
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR. pp. 770–778 (2016)
2016
Earlier work this paper cites.
Laina, I., Rupprecht, C., Belagiannis, V., Tombari, F., Navab, N.: Deeper depth prediction with fully convolutional residual networks. In: 2016 Fourth International Conference on 3D Vision (3DV). pp. 239–248 (2016)
2016
Earlier work this paper cites.
Liu, F., Shen, C., Lin, G., Reid, I.: Learning depth from single monocular images using deep convolutional neural fields. IEEE Transactions on Pattern Analysis and Machine Intelligence 38
2016
Earlier work this paper cites.
Godard, C., Aodha, O.M., Brostow, G.J.: Unsupervised monocular depth estimation with left-right consistency. In: CVPR. pp. 6602–6611 (2017)
2017
Cited alongside, same era.
Kuznietsov, Y., Stückler, J., Leibe, B.: Semi-supervised deep learning for monocular depth map prediction. In: CVPR. pp. 2215–2223 (2017)
2017
Cited alongside, same era.
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E.: Automatic differentiation in pytorch (2017)
2017
Cited alongside, same era.
Uhrig, J., Schneider, N., Schneider, L., Franke, U., Brox, T., Geiger, A.: Sparsity invariant cnns. In: 2017 International Conference on 3D Vision, 3DV 2017, Qingdao, China, October 10-12, 2017. pp. 11–20 (2017)
2017
Cited alongside, same era.
Zhou, T., Brown, M., Snavely, N., Lowe, D.G.: Unsupervised learning of depth and ego-motion from video. In: CVPR. pp. 6612–6619 (2017)
2017
Zou, Y., Luo, Z., Huang, J.: Df-net: Unsupervised joint learning of depth and flow using cross-task consistency. In: ECCV. vol. 11209, pp. 38–55 (2018)
2018
Later among the works it cites.
Bian, J.W., Li, Z., Wang, N., Zhan, H., Shen, C., Cheng, M.M., Reid, I.: Unsupervised scale-consistent depth and ego-motion learning from monocular video. In: NeurIPS (2019)
2019
Later among the works it cites.
Bozorgtabar, B., Rad, M.S., Mahapatra, D., Thiran, J.P.: Syndemo: Synergistic deep feature alignment for joint learning of depth and ego-motion. In: ICCV (2019)
2019
Later among the works it cites.
Casser, V., Pirk, S., Mahjourian, R., Angelova, A.: Depth prediction without the sensors: Leveraging structure for unsupervised learning from monocular videos. In: AAAI. pp. 8001–8008 (2019)
2019
Later among the works it cites.
Chen, B., Deng, W., Hu, J.: Mixed high-order attention network for person re-identification. In: ICCV (2019)
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Cited alongside, same era.
Abarghouei, A.A., Breckon, T.P.: Real-time monocular depth estimation using synthetic data with domain adaptation via image style transfer. In: CVPR. pp. 2800–2810 (2018)
2018
Cited alongside, same era.
Fu, H., Gong, M., Wang, C., Batmanghelich, K., Tao, D.: Deep ordinal regression network for monocular depth estimation. In: CVPR. pp. 2002–2011 (2018)
2018
Cited alongside, same era.
Guo, X., Li, H., Yi, S., Ren, J.S.J., Wang, X.: Learning monocular depth by distilling cross-domain stereo networks. In: ECCV. vol. 11215, pp. 506–523 (2018)
2018
Cited alongside, same era.
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: CVPR. pp. 7132–7141 (2018)
2018
Cited alongside, same era.
Li, R., Wang, S., Long, Z., Gu, D.: Undeepvo: Monocular visual odometry through unsupervised deep learning. In: ICRA. pp. 7286–7291. IEEE (2018)
2018
Cited alongside, same era.
Mahjourian, R., Wicke, M., Angelova, A.: Unsupervised learning of depth and ego-motion from monocular video using 3d geometric constraints. In: CVPR. pp. 5667–5675 (2018)
2018
Cited alongside, same era.
Wang, C., Buenaposada, J.M., Zhu, R., Lucey, S.: Learning depth from monocular videos using direct methods. In: CVPR. pp. 2022–2030 (2018)
2018
Cited alongside, same era.
2019
Later among the works it cites.
Chen, Y., Schmid, C., Sminchisescu, C.: Self-supervised learning with geometric constraints in monocular video: Connecting flow, depth, and camera. In: ICCV (2019)
2019
Later among the works it cites.
Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., Lu, H.: Dual attention network for scene segmentation. In: CVPR. pp. 3146–3154 (2019)
2019
Later among the works it cites.
Godard, C., Mac Aodha, O., Firman, M., Brostow, G.J.: Digging into self-supervised monocular depth prediction. In: ICCV (2019)
2019
Later among the works it cites.
Hu, J., Ozay, M., Zhang, Y., Okatani, T.: Revisiting single image depth estimation: Toward higher resolution maps with accurate object boundaries. In: 2019 IEEE Winter Conference on Applications of Computer Vision (WACV). pp. 1043–1051 (2019)
2019
Later among the works it cites.
Luo, C., Yang, Z., Wang, P., Wang, Y., Xu, W., Nevatia, R., Yuille, A.L.: Every pixel counts ++: Joint learning of geometry and motion with 3d holistic understanding. TPAMI (2019)
2019
Later among the works it cites.
Ranjan, A., Jampani, V., Balles, L., Kim, K., Sun, D., Wulff, J., Black, M.J.: Competitive collaboration: Joint unsupervised learning of depth, camera motion, optical flow and motion segmentation. In: CVPR. pp. 12232–12241 (2019)
2019
Later among the works it cites.
Watson, J., Firman, M., Brostow, G.J., Turmukhambetov, D.: Self-supervised monocular depth hints. In: ICCV (2019)
2019
Later among the works it cites.
Zhang, H., Shen, C., Li, Y., Cao, Y., Liu, Y., Yan, Y.: Exploiting temporal consistency for real-time video depth estimation (2019)
2019
Later among the works it cites.
Zhou, J., Wang, Y., Qin, K., Zeng, W.: Unsupervised high-resolution depth learning from videos with dual networks. In: ICCV (2019)
2019
Later among the works it cites.