Fetching the paper…
Reading the bibliography…
Monocular depth estimation is a fundamental task in computer vision and has drawn increasing attention.
Silberman, N., Hoiem, D., Kohli, P., Fergus, R.: Indoor segmentation and support inference from rgbd images. In: European Conference on Computer Vision (ECCV). pp. 746–760. Springer (2012)
2012
Earlier work this paper cites.
Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: Vision meets robotics: The kitti dataset. The International Journal of Robotics Research 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: Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems (NIPS). vol. 27 (2014)
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
Song, S., Lichtenberg, S.P., Xiao, J.: Sun rgb-d: A rgb-d scene understanding benchmark suite. In: Computer Vision and Pattern Recognition (CVPR). pp. 567–576 (2015)
2015
Earlier work this paper cites.
Song, S., Lichtenberg, S.P., Xiao, J.: Sun rgb-d: A rgb-d scene understanding benchmark suite. In: Computer Vision and Pattern Recognition (CVPR). pp. 567–576 (2015)
2015
Earlier work this paper cites.
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 (ECCV). 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: Computer Vision and Pattern Recognition (CVPR). pp. 770–778 (2016)
2016
Earlier work this paper cites.
Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE transactions on pattern analysis and machine intelligence (TPAMI) 40
2017
Earlier work this paper cites.
Godard, C., Mac Aodha, O., Brostow, G.J.: Unsupervised monocular depth estimation with left-right consistency. In: Computer Vision and Pattern Recognition (CVPR). pp. 270–279 (2017)
2017
Earlier work this paper cites.
Kendall, A., Gal, Y.: What uncertainties do we need in bayesian deep learning for computer vision? In: Neural Information Processing Systems (NIPS). pp. 5574–5584 (2017)
2017
Earlier work this paper cites.
Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Computer Vision and Pattern Recognition (CVPR). pp. 2117–2125 (2017)
2017
Earlier work this paper cites.
Uhrig, J., Schneider, N., Schneider, L., Franke, U., Brox, T., Geiger, A.: Sparsity invariant cnns. In: International Conference on 3D Vision (3DV). pp. 11–20. IEEE (2017)
2017
Earlier work this paper cites.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Advances in neural information processing systems (NIPS). pp. 5998–6008 (2017)
2017
Earlier work this paper cites.
2018
Earlier work this paper cites.
Fu, H., Gong, M., Wang, C., Batmanghelich, K., Tao, D.: Deep ordinal regression network for monocular depth estimation. In: Computer Vision and Pattern Recognition (CVPR). pp. 2002–2011 (2018)
2018
Earlier work this paper cites.
Gan, Y., Xu, X., Sun, W., Lin, L.: Monocular depth estimation with affinity, vertical pooling, and label enhancement. In: European Conference on Computer Vision (ECCV). pp. 224–239 (2018)
2018
Cited alongside, same era.
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Computer Vision and Pattern Recognition (CVPR). pp. 7132–7141 (2018)
2018
Cited alongside, same era.
Woo, S., Park, J., Lee, J.Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: European Conference on Computer Vision (ECCV). pp. 3–19 (2018)
2018
Cited alongside, same era.
2019
Cited alongside, same era.
Diaz, R., Marathe, A.: Soft labels for ordinal regression. In: Computer Vision and Pattern Recognition (CVPR). pp. 4738–4747 (2019)
Johnston, A., Carneiro, G.: Self-supervised monocular trained depth estimation using self-attention and discrete disparity volume. In: Computer Vision and Pattern Recognition (CVPR). pp. 4756–4765 (2020)
2020
Later among the works it cites.
Wang, L., Zhang, J., Wang, Y., Lu, H., Ruan, X.: Cliffnet for monocular depth estimation with hierarchical embedding loss. In: European Conference on Computer Vision (ECCV). pp. 316–331 (2020)
2020
Later among the works it cites.
Xu, D., Alameda-Pineda, X., Ouyang, W., Ricci, E., Wang, X., Sebe, N.: Probabilistic graph attention network with conditional kernels for pixel-wise prediction. Transactions on Pattern Analysis and Machine Intelligence (TPAMI) (2020)
2020
Later among the works it cites.
Bhat, S.F., Alhashim, I., Wonka, P.: Adabins: Depth estimation using adaptive bins. In: Computer Vision and Pattern Recognition (CVPR). pp. 4009–4018 (2021)
2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2019
Cited alongside, same era.
Godard, C., Mac Aodha, O., Firman, M., Brostow, G.J.: Digging into self-supervised monocular depth estimation. In: International Conference on Computer Vision (ICCV). pp. 3828–3838 (2019)
2019
Cited alongside, same era.
Lee, J.H., Kim, C.S.: Monocular depth estimation using relative depth maps. In: Computer Vision and Pattern Recognition (CVPR). pp. 9729–9738 (2019)
2019
Cited alongside, same era.
2019
Cited alongside, same era.
Liu, C., Gu, J., Kim, K., Narasimhan, S.G., Kautz, J.: Neural rgb (r) d sensing: Depth and uncertainty from a video camera. In: Computer Vision and Pattern Recognition (CVPR). pp. 10986–10995 (2019)
2019
Cited alongside, same era.
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems (NIPS) 32
2019
Cited alongside, same era.
Tan, M., Le, Q.: Efficientnet: Rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning (ICML). pp. 6105–6114 (2019)
2019
Cited alongside, same era.
Yin, W., Liu, Y., Shen, C., Yan, Y.: Enforcing geometric constraints of virtual normal for depth prediction. In: International Conference on Computer Vision (ICCV). pp. 5684–5693 (2019)
2019
Cited alongside, same era.
2021
Later among the works it cites.
Cheng, B., Schwing, A., Kirillov, A.: Per-pixel classification is not all you need for semantic segmentation. Advances in Neural Information Processing Systems (NIPS) 34
2021
Later among the works it cites.
Jung, H., Park, E., Yoo, S.: Fine-grained semantics-aware representation enhancement for self-supervised monocular depth estimation. In: International Conference on Computer Vision (ICCV). pp. 12642–12652 (2021)
2021
Later among the works it cites.
Lee, S., Lee, J., Kim, B., Yi, E., Kim, J.: Patch-wise attention network for monocular depth estimation. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 35, pp. 1873–1881 (2021)
2021
Later among the works it cites.
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: International Conference on Computer Vision (ICCV). pp. 10012–10022 (2021)
2021
Later among the works it cites.
Lo, C.C., Vandewalle, P.: Depth estimation from monocular images and sparse radar using deep ordinal regression network. In: International Conference on Image Processing (ICIP). pp. 3343–3347 (2021)
2021
Later among the works it cites.
Qiao, S., Zhu, Y., Adam, H., Yuille, A., Chen, L.C.: Vip-deeplab: Learning visual perception with depth-aware video panoptic segmentation. In: Computer Vision and Pattern Recognition (CVPR). pp. 3997–4008 (2021)
2021
Later among the works it cites.
Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: International Conference on Computer Vision (ICCV). pp. 12179–12188 (2021)
2021
Later among the works it cites.
Zhao, J., Yan, K., Zhao, Y., Guo, X., Huang, F., Li, J.: Transformer-based dual relation graph for multi-label image recognition. In: International Conference on Computer Vision (ICCV). pp. 163–172 (2021)
2021
Later among the works it cites.
Zheng, S., Lu, J., Zhao, H., Zhu, X., Luo, Z., Wang, Y., Fu, Y., Feng, J., Xiang, T., Torr, P.H., et al.: Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. In: Computer Vision and Pattern Recognition (CVPR). pp. 6881–6890 (2021)
2021
Later among the works it cites.
Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. In: International Conference on Learning Representations (ICLR) (2021)
2021
Later among the works it cites.