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Purpose: Depth estimation in robotic surgery is vital in 3D reconstruction, surgical navigation and augmented reality visualization.
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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, pp. 1851–1858 (2017)
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Li, Z., Snavely, N.: Megadepth: Learning single-view depth prediction from internet photos. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2041–2050 (2018)
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Liu, X., Sinha, A., Ishii, M., Hager, G.D., Reiter, A., Taylor, R.H., Unberath, M.: Dense depth estimation in monocular endoscopy with self-supervised learning methods. IEEE transactions on medical imaging 39
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Bian, J., Li, Z., Wang, N., Zhan, H., Shen, C., Cheng, M.-M., Reid, I.: Unsupervised scale-consistent depth and ego-motion learning from monocular video. Advances in neural information processing systems 32
2019
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Godard, C., Mac Aodha, O., Firman, M., Brostow, G.J.: Digging into self-supervised monocular depth estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3828–3838 (2019)
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Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., Liu, P.J.: Exploring the limits of transfer learning with a unified text-to-text transformer. The Journal of Machine Learning Research 21
2020
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Fang, Z., Chen, X., Chen, Y., Gool, L.V.: Towards good practice for cnn-based monocular depth estimation. In: Proceedings of the IEEE Winter Conference on Applications of Computer Vision, pp. 1091–1100 (2020)
2020
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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)
2020
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2021
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, pp. 4009–4018 (2021)
2021
Cited alongside, same era.
Shao, S., Pei, Z., Chen, W., Zhu, W., Wu, X., Sun, D., Zhang, B.: Self-supervised monocular depth and ego-motion estimation in endoscopy: Appearance flow to the rescue. Medical image analysis 77
2022
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Zha, R., Cheng, X., Li, H., Harandi, M., Ge, Z.: Endosurf: Neural surface reconstruction of deformable tissues with stereo endoscope videos. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 13–23 (2023). Springer
2023
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2023
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2021
Cited alongside, same era.
Recasens, D., Lamarca, J., Fácil, J.M., Montiel, J., Civera, J.: Endo-depth-and-motion: Reconstruction and tracking in endoscopic videos using depth networks and photometric constraints. IEEE Robotics and Automation Letters 6
2021
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Ozyoruk, K.B., Gokceler, G.I., Bobrow, T.L., Coskun, G., Incetan, K., Almalioglu, Y., Mahmood, F., Curto, E., Perdigoto, L., Oliveira, M., Sahin, H., Araujo, H., Alexandrino, H., Durr, N.J., Gibert, H.B., Mehmet, T.: Endoslam dataset and an unsupervised monocular visual odometry and depth estimation approach for endoscopic videos. Medical image analysis 71
2021
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Wei, X., Wang, Y., Ge, L., Peng, B., He, Q., Wang, R., Huang, L., Xu, Y., Luo, J.: Unsupervised convolutional neural network for motion estimation in ultrasound elastography. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control 69
2022
Cited alongside, same era.
Wang, Y., Long, Y., Fan, S.H., Dou, Q.: Neural rendering for stereo 3d reconstruction of deformable tissues in robotic surgery. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 431–441 (2022). Springer
2022
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2023
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2023
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2023
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Wu, Q., Zhang, Y., Elbatel, M.: Self-prompting large vision models for few-shot medical image segmentation. In: MICCAI Workshop on Domain Adaptation and Representation Transfer, pp. 156–167 (2023). Springer
2023
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2023
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