Fetching the paper…
Reading the bibliography…
We present a deep model that can accurately produce dense depth maps given an RGB image with known depth at a very sparse set of pixels.
Levin, A., Lischinski, D., Weiss, Y.: Colorization using optimization. In: ACM Transactions on Graphics (ToG). vol. 23, pp. 689–694. ACM (2004)
2004
Earlier work this paper cites.
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International journal of computer vision 60
2004
Earlier work this paper cites.
Sinz, F.H., Candela, J.Q., Bakır, G.H., Rasmussen, C.E., Franz, M.O.: Learning depth from stereo. In: Joint Pattern Recognition Symposium. pp. 245–252. Springer (2004)
2004
Earlier work this paper cites.
Saxena, A., Chung, S.H., Ng, A.Y.: Learning depth from single monocular images. In: Advances in neural information processing systems. pp. 1161–1168 (2006)
2006
Earlier work this paper cites.
Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: Orb: An efficient alternative to sift or surf. In: Computer Vision (ICCV), 2011 IEEE international conference on. pp. 2564–2571. IEEE (2011)
2011
Earlier work this paper cites.
Khoshelham, K., Elberink, S.O.: Accuracy and resolution of kinect depth data for indoor mapping applications. Sensors 12
2012
Earlier work this paper cites.
Nathan Silberman, Derek Hoiem, P.K., Fergus, R.: Indoor segmentation and support inference from rgbd images. In: ECCV (2012)
2012
Earlier work this paper cites.
Nguyen, C.V., Izadi, S., Lovell, D.: Modeling kinect sensor noise for improved 3d reconstruction and tracking. In: 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT), 2012 Second International Conference on. pp. 524–530. IEEE (2012)
2012
Earlier work this paper cites.
Kerl, C., Sturm, J., Cremers, D.: Dense visual slam for rgb-d cameras. In: Intelligent Robots and Systems (IROS), 2013 IEEE/RSJ International Conference on. pp. 2100–2106. IEEE (2013)
2013
Earlier work this paper cites.
Lin, D., Fidler, S., Urtasun, R.: Holistic scene understanding for 3d object detection with rgbd cameras. In: Computer Vision (ICCV), 2013 IEEE International Conference on. pp. 1417–1424. IEEE (2013)
2013
Earlier work this paper cites.
Song, S., Xiao, J.: Tracking revisited using rgbd camera: Unified benchmark and baselines. In: Computer Vision (ICCV), 2013 IEEE International Conference on. pp. 233–240. IEEE (2013)
2013
Earlier work this paper cites.
Teichman, A., Lussier, J.T., Thrun, S.: Learning to segment and track in rgbd. IEEE Transactions on Automation Science and Engineering 10
2013
Earlier work this paper cites.
Hermans, A., Floros, G., Leibe, B.: Dense 3d semantic mapping of indoor scenes from rgb-d images. In: Robotics and Automation (ICRA), 2014 IEEE International Conference on. pp. 2631–2638. IEEE (2014)
2014
Earlier work this paper cites.
2014
Cited alongside, same era.
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 (2015)
2015
Cited alongside, same era.
2015
Cited alongside, same era.
Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: International conference on machine learning. pp. 448–456 (2015)
2015
Cited alongside, same era.
Laina, I., Rupprecht, C., Belagiannis, V., Tombari, F., Navab, N.: Deeper depth prediction with fully convolutional residual networks. In: 3D Vision (3DV), 2016 Fourth International Conference on. pp. 239–248. IEEE (2016)
2016
Later among the works it cites.
2016
Later among the works it cites.
Song, X., Dai, Y., Qin, X.: Deep depth super-resolution: Learning depth super-resolution using deep convolutional neural network. In: Asian Conference on Computer Vision. pp. 360–376. Springer (2016)
2016
Later among the works it cites.
2016
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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 (2015)
2015
Cited alongside, same era.
Lu, J., Forsyth, D.A., et al.: Sparse depth super resolution. In: CVPR. vol. 6 (2015)
2015
Cited alongside, same era.
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A., et al.: Going deeper with convolutions. Cvpr (2015)
2015
Cited alongside, same era.
Whelan, T., Kaess, M., Johannsson, H., Fallon, M., Leonard, J.J., McDonald, J.: Real-time large-scale dense rgb-d slam with volumetric fusion. The International Journal of Robotics Research 34
2015
Cited alongside, same era.
Chen, W., Fu, Z., Yang, D., Deng, J.: Single-image depth perception in the wild. In: Advances in Neural Information Processing Systems. pp. 730–738 (2016)
2016
Cited alongside, same era.
Garg, R., BG, V.K., Carneiro, G., Reid, I.: Unsupervised cnn for single view depth estimation: Geometry to the rescue. In: European Conference on Computer Vision. pp. 740–756. Springer (2016)
2016
Cited alongside, same era.
Horaud, R., Hansard, M., Evangelidis, G., Ménier, C.: An overview of depth cameras and range scanners based on time-of-flight technologies. Machine Vision and Applications 27
2016
Cited alongside, same era.
Hui, T.W., Loy, C.C., Tang, X.: Depth map super-resolution by deep multi-scale guidance. In: European Conference on Computer Vision. pp. 353–369. Springer (2016)
2016
Cited alongside, same era.
Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39
2017
Later among the works it cites.
2017
Later among the works it cites.
Huang, G., Liu, Z., Weinberger, K.Q., van der Maaten, L.: Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. vol. 1, p. 3 (2017)
2017
Later among the works it cites.
2017
Later among the works it cites.
Kuznietsov, Y., Stückler, J., Leibe, B.: Semi-supervised deep learning for monocular depth map prediction. In: Proc. of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 6647–6655 (2017)
2017
Later among the works it cites.
2017
Later among the works it cites.
Uhrig, J., Schneider, N., Schneider, L., Franke, U., Brox, T., Geiger, A.: Sparsity invariant cnns. In: International Conference on 3D Vision (3DV) (2017)
2017
Later among the works it cites.
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 CVPR (2017)
2017
Later among the works it cites.