Fetching the paper…
Reading the bibliography…
Monocular depth estimation is a crucial task in computer vision.
Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. International journal of computer vision 47
2002
Earlier work this paper cites.
Hartley, R., Zisserman, A.: Multiple view geometry in computer vision. Cambridge university press (2003)
2003
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. (eds.) Advances in Neural Information Processing Systems. vol. 27. Curran Associates, Inc. (2014), https://proceedings.neurips.cc/paper_files/paper/2014/file/7bccfde7714a1ebadf06c5f4cea752c1-Paper.pdf
2014
Earlier work this paper cites.
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 (ICCV) (December 2015)
2015
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
2015
Earlier work this paper cites.
Garg, R., B.G., V.K., 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.) Computer Vision – ECCV 2016. pp. 740–756. Springer International Publishing, Cham (2016)
2016
Earlier work this paper cites.
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 (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. IEEE (2016)
2016
Earlier work this paper cites.
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 (CVPR) (July 2017)
2017
Earlier work this paper cites.
Maddern, W., Pascoe, G., Linegar, C., Newman, P.: 1 Year, 1000km: The Oxford RobotCar Dataset. The International Journal of Robotics Research (IJRR) 36
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. Advances in neural information processing systems 30
2017
Earlier work this paper cites.
Zhou, T., Brown, M., Snavely, N., Lowe, D.G.: Unsupervised learning of depth and ego-motion from video. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (July 2017)
2017
Earlier work this paper cites.
Fu, H., Gong, M., Wang, C., Batmanghelich, K., Tao, D.: Deep ordinal regression network for monocular depth estimation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 2002–2011 (2018)
2018
Earlier work this paper cites.
Godard, C., Mac Aodha, O., Firman, M., Brostow, G.J.: Digging into self-supervised monocular depth estimation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (October 2019)
2019
Earlier work this paper cites.
Caesar, H., Bankiti, V., Lang, A.H., Vora, S., Liong, V.E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., Beijbom, O.: nuscenes: A multimodal dataset for autonomous driving. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 11621–11631 (2020)
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
Guizilini, V., Ambrus, R., Pillai, S., Raventos, A., Gaidon, A.: 3d packing for self-supervised monocular depth estimation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 2485–2494 (2020)
2020
Cited alongside, same era.
Lin, J.T., Dai, D., Gool, L.V.: Depth estimation from monocular images and sparse radar data. In: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). pp. 10233–10240 (2020). https://doi.org/10.1109/IROS45743.2020.9340998
2020
Cited alongside, same era.
Ranftl, R., Lasinger, K., Hafner, D., Schindler, K., Koltun, V.: Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer. IEEE transactions on pattern analysis and machine intelligence 44
2020
Cited alongside, same era.
Spencer, J., Bowden, R., Hadfield, S.: Defeat-net: General monocular depth via simultaneous unsupervised representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 14402–14413 (2020)
Zhao, C., Tang, Y., Sun, Q.: Unsupervised monocular depth estimation in highly complex environments. IEEE Transactions on Emerging Topics in Computational Intelligence 6
2022
Later among the works it cites.
Zhao, C., Zhang, Y., Poggi, M., Tosi, F., Guo, X., Zhu, Z., Huang, G., Tang, Y., Mattoccia, S.: Monovit: Self-supervised monocular depth estimation with a vision transformer. In: 2022 International Conference on 3D Vision (3DV). pp. 668–678. IEEE (2022)
2022
Later among the works it cites.
2023
Later among the works it cites.
Gasperini, S., Morbitzer, N., Jung, H., Navab, N., Tombari, F.: Robust monocular depth estimation under challenging conditions. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). pp. 8177–8186 (October 2023)
2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2020
Cited alongside, same era.
Vankadari, M., Garg, S., Majumder, A., Kumar, S., Behera, A.: Unsupervised monocular depth estimation for night-time images using adversarial domain feature adaptation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.M. (eds.) Computer Vision – ECCV 2020. pp. 443–459. Springer International Publishing, Cham (2020)
2020
Cited alongside, same era.
Bhat, S.F., Alhashim, I., Wonka, P.: Adabins: Depth estimation using adaptive bins. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 4009–4018 (June 2021)
2021
Cited alongside, same era.
Gasperini, S., Koch, P., Dallabetta, V., Navab, N., Busam, B., Tombari, F.: R4dyn: Exploring radar for self-supervised monocular depth estimation of dynamic scenes. In: 2021 International Conference on 3D Vision (3DV). pp. 751–760 (2021). https://doi.org/10.1109/3DV53792.2021.00084
2021
Cited alongside, same era.
Liu, L., Song, X., Wang, M., Liu, Y., Zhang, L.: Self-supervised monocular depth estimation for all day images using domain separation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 12737–12746 (2021)
2021
Cited alongside, same era.
Liu, L., Song, X., Wang, M., Liu, Y., Zhang, L.: Self-supervised monocular depth estimation for all day images using domain separation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). pp. 12737–12746 (October 2021)
2021
Cited alongside, same era.
Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 12179–12188 (2021)
2021
Cited alongside, same era.
Wang, K., Zhang, Z., Yan, Z., Li, X., Xu, B., Li, J., Yang, J.: Regularizing nighttime weirdness: Efficient self-supervised monocular depth estimation in the dark. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). pp. 16055–16064 (October 2021)
2021
Cited alongside, same era.
Yin, W., Liu, Y., Shen, C.: Virtual normal: Enforcing geometric constraints for accurate and robust depth prediction. IEEE Transactions on Pattern Analysis and Machine Intelligence 44
2021
Cited alongside, same era.
Li, J., Wang, Y., Huang, Z., Zheng, J., Xian, K., Cao, Z., Zhang, J.: Diffusion-augmented depth prediction with sparse annotations. In: Proceedings of the 31st ACM International Conference on Multimedia. p. 2865–2876. MM ’23, Association for Computing Machinery, New York, NY, USA (2023). https://doi.org/10.1145/3581783.3611807, https://doi.org/10.1145/3581783.3611807
2023
Later among the works it cites.
2023
Later among the works it cites.
Oquab, M., Darcet, T., Moutakanni, T., Vo, H.V., Szafraniec, M., Khalidov, V., Fernandez, P., Haziza, D., Massa, F., El-Nouby, A., Howes, R., Huang, P.Y., Xu, H., Sharma, V., Li, S.W., Galuba, W., Rabbat, M., Assran, M., Ballas, N., Synnaeve, G., Misra, I., Jegou, H., Mairal, J., Labatut, P., Joulin, A., Bojanowski, P.: Dinov2: Learning robust visual features without supervision (2023)
2023
Later among the works it cites.
Saunders, K., Vogiatzis, G., Manso, L.J.: Self-supervised monocular depth estimation: Let’s talk about the weather. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). pp. 8907–8917 (October 2023)
2023
Later among the works it cites.
Shin, U., Park, J., Kweon, I.S.: Deep depth estimation from thermal image. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 1043–1053 (June 2023)
2023
Later among the works it cites.
Vankadari, M., Golodetz, S., Garg, S., Shin, S., Markham, A., Trigoni, N.: When the sun goes down: Repairing photometric losses for all-day depth estimation. In: Liu, K., Kulic, D., Ichnowski, J. (eds.) Proceedings of The 6th Conference on Robot Learning. Proceedings of Machine Learning Research, vol. 205, pp. 1992–2003. PMLR (14–18 Dec 2023), https://proceedings.mlr.press/v205/vankadari23a.html
2023
Later among the works it cites.
2023
Later among the works it cites.
Ye, H., Zhang, J., Liu, S., Han, X., Yang, W.: Ip-adapter: Text compatible image prompt adapter for text-to-image diffusion models (2023)
2023
Later among the works it cites.
Zhang, L., Rao, A., Agrawala, M.: Adding conditional control to text-to-image diffusion models. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). pp. 3836–3847 (October 2023)
2023
Later among the works it cites.
Xian, K., Cao, Z., Shen, C., Lin, G.: Towards robust monocular depth estimation: A new baseline and benchmark. International Journal of Computer Vision pp. 1–19 (2024)
2024
Closest in time.