Abstract
Depth cues are vital in many challenging computer vision tasks. In this paper, we address the problem of dense depth prediction from a single RGB image. Compared with stereo depth estimation, sensing the depth of a scene from monocular images is much more difficult and ambiguous because the epipolar geometry constraints cannot be exploited. In addition, the value of the scale is often unknown in monocular depth prediction. To facilitate an accurate single-view depth prediction, we introduce dilated convolution to capture multi-scale contextual information and then present a deep convolutional neural network. To improve the robustness of the system, we estimate the uncertainty of noisy data by modelling such uncertainty in a new loss function. The experiment results show that the proposed approach outperforms the previous state-of-the-art methods in depth estimation tasks.
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References
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440. IEEE, Boston (2015)
Saxena, A., Sun, M., Ng, A.Y.: Make3D: learning 3D scene structure from a single still image. IEEE Trans. Pattern Anal. Mach. Intell. 31(5), 824–840 (2009)
Taylor, J., Shotton, J., Sharp, T., Fitzgibbon, A.: The Vitruvian manifold: inferring dense correspondences for one-shot human pose estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 103–110. IEEE, Providence (2012)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778. IEEE, Las Vegas (2016)
Bertasius, G., Shi, J., Torresani, L.: DeepEdge: a multi-scale bifurcated deep network for top-down contour detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4380–4389. IEEE, Boston (2015)
Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1–3), 7–42 (2002)
Szeliski, R.: Structure from motion. Computer Vision. Texts in Computer Science, pp. 303–334. Springer, London (2011). https://doi.org/10.1007/978-1-84882-935-7
LeCun, Y., et al.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541–551 (1989)
Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. In: International Conference on Learning Representations, Caribe Hilton, Puerto Rico (2016)
Saxena, A., Chung, S.H., Ng, A.Y.: Learning depth from single monocular images. In: International Conference on Neural Information Processing Systems, pp. 1161–1168. MIT Press, Vancouver (2005)
Ladicky, L., Shi, J., Pollefeys, M.: Pulling things out of perspective. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 89–96. IEEE, Columbus (2014)
Karsch, K., Liu, C., Kang, S.B.: Depth extraction from video using non-parametric sampling. In: Fitzgibbon, A., et al. (eds.) ECCV 2012. LNCS, vol. 7576, pp. 775–788. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33715-4_56
Eigen, D., Puhrsch, C., Fergus, R.: Prediction from a single image using a multi-scale deep network. In: International Conference on Neural Information Processing Systems, pp. 2366–2374. MIT Press, Montreal (2014)
Eigen, D., Fergus, R.: Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2650–2658. IEEE, Santiago (2015)
Liu, F., Shen, C., Lin, G.: Deep convolutional neural fields for depth estimation from a single image. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5162–5170. IEEE, Boston (2015)
Wang, P., Shen, X., Lin, Z., Cohen, S., Price, B., Yuille, A.L.: Towards unified depth and semantic prediction from a single image. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2800–2809. IEEE, Boston (2015)
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. IEEE, Stanford (2016)
Garg, R., Vijay Kumar, B.G., Carneiro, G., Reid, I.: Unsupervised CNN for single view depth estimation: geometry to the rescue. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 740–756. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46484-8_45
Godard, C., Mac Aodha, O., Brostow, G.J.: Unsupervised monocular depth estimation with left-right consistency. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6602–6611. IEEE, Honolulu (2017)
Liu, M., Salzmann, M., He, X.: Discrete-continuous depth estimation from a single image. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 716–723. IEEE, Columbus (2014)
Silberman, N., Hoiem, D., Kohli, P., Fergus, R.: Indoor segmentation and support inference from RGBD images. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7576, pp. 746–760. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33715-4_54
Paszke, A., Chaurasia, A., Kim, S., Culurciello, E.: ENet: a deep neural network architecture for real-time semantic segmentation. CoRR abs/1606.02147 (2016). http://arxiv.org/abs/1606.02147
Russakovsky, O., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)
Roy, A., Todorovic, S.: Monocular depth estimation using neural regression forest. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5506–5514. IEEE, Las Vegas (2016)
Xu, D., Ricci, E., Ouyang, W., Wang, X., Sebe, N.: Multi-scale continuous crfs as sequential deep networks for monocular depth estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 161–169. IEEE, Honolulu (2017)
Li, B., Shen, C., Dai, Y., van den Hengel, A., He, M.: Depth and surface normal estimation from monocular images using regression on deep features and hierarchical CRFs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1119–1127. IEEE, Boston (2015)
Acknowledgements
This work was supported in part by Zhejiang Provincial Natural Science Foundation of China (Grant No. LY18F010004).
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Ma, H., Ding, Y., Wang, L., Zhang, M., Li, D. (2018). Depth Estimation from Monocular Images Using Dilated Convolution and Uncertainty Learning. In: Hong, R., Cheng, WH., Yamasaki, T., Wang, M., Ngo, CW. (eds) Advances in Multimedia Information Processing – PCM 2018. PCM 2018. Lecture Notes in Computer Science(), vol 11165. Springer, Cham. https://doi.org/10.1007/978-3-030-00767-6_2
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