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This work delves into unsupervised monocular depth estimation in endoscopy, which leverages adjacent frames to establish a supervisory signal during the training phase.
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Direct sparse odometry
Engel, J., Koltun, V., Cremers, D., 2017 · 2017
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Unsupervised monocular depth estimation with left-right consistency, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 270–279
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Automatic differentiation in pytorch
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Deep monocular 3d reconstruction for assisted navigation in bronchoscopy
Visentini-Scarzanella, M., Sugiura, T., Kaneko, T., Koto, S., 2017 · 2017
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A gift from knowledge distillation: Fast optimization, network minimization and transfer learning, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4133–4141
Don’t forget the past: Recurrent depth estimation from monocular video
Patil, V., Van Gansbeke, W., Dai, D., Van Gool, L., 2020 · 2020
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Defeat-net: general monocular depth via simultaneous unsupervised representation learning, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 14402–14413
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Xie, Q., Dai, Z., Hovy, E., Luong, T., Le, Q., 2020 · 2020
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P2̂net: Patch-match and plane-regularization for unsupervised indoor depth estimation, in: Proceedings of the European Conference on Computer Vision, Springer. pp. 206–222
Yu, Z., Jin, L., Gao, S., 2020 · 2020
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Stereo correspondence and reconstruction of endoscopic data challenge
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Yim, J., Joo, D., Bae, J., Kim, J., 2017 · 2017
Cited alongside, same era.
Unsupervised learning of depth and ego-motion from video, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1851–1858
Zhou, T., Brown, M., Snavely, N., Lowe, D.G., 2017 · 2017
Cited alongside, same era.
Slam-based dense surface reconstruction in monocular minimally invasive surgery and its application to augmented reality
Chen, L., Tang, W., John, N.W., Wan, T.R., Zhang, J.J., 2018 · 2018
Cited alongside, same era.
Evaluation and stability analysis of video-based navigation system for functional endoscopic sinus surgery on in vivo clinical data
Leonard, S., Sinha, A., Reiter, A., Ishii, M., Gallia, G.L., Taylor, R.H., Hager, G.D., 2018 · 2018
Cited alongside, same era.
Unsupervised reverse domain adaptation for synthetic medical images via adversarial training
Mahmood, F., Chen, R., Durr, N.J., 2018 · 2018
Cited alongside, same era.
Deep learning and conditional random fields-based depth estimation and topographical reconstruction from conventional endoscopy
Mahmood, F., Durr, N.J., 2018 · 2018
Cited alongside, same era.
Endoscopic navigation in the absence of ct imaging, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer. pp. 64–71
Sinha, A., Liu, X., Reiter, A., Ishii, M., Hager, G.D., Taylor, R.H., 2018 · 2018
Cited alongside, same era.
Unsupervised odometry and depth learning for endoscopic capsule robots, in: IEEE International Conference on Intelligent Robots and Systems, IEEE. pp. 1801–1807
Turan, M., Ornek, E.P., Ibrahimli, N., Giracoglu, C., Almalioglu, Y., Yanik, M.F., Sitti, M., 2018 · 2018
Cited alongside, same era.
Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., 2021 · 2021
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Revealing the reciprocal relations between self-supervised stereo and monocular depth estimation, in: Proceedings of the IEEE International Conference on Computer Vision, pp. 15529–15538
Chen, Z., Ye, X., Yang, W., Xu, Z., Tan, X., Zou, Z., Ding, E., Zhang, X., Huang, L., 2021 · 2021
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Serv-ct: A disparity dataset from cone-beam cone-beam ct for validation of endoscopic 3d reconstruction
Edwards, P.E., Psychogyios, D., Speidel, S., Maier-Hein, L., Stoyanov, D., 2021 · 2021
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Endoslam dataset and an unsupervised monocular visual odometry and depth estimation approach for endoscopic videos: Endo-sfmlearner
Ozyoruk, K.B., Gokceler, G.I., Bobrow, T.L., Coskun, G., Incetan, K., Almalioglu, Y., Mahmood, F., Curto, E., Perdigoto, L., Oliveira, M., et al., 2021 · 2021
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Excavating the potential capacity of self-supervised monocular depth estimation, in: Proceedings of the IEEE International Conference on Computer Vision, pp. 15560–15569
Peng, R., Wang, R., Lai, Y., Tang, L., Cai, Y., 2021 · 2021
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Endo-depth-and-motion: Reconstruction and tracking in endoscopic videos using depth networks and photometric constraints
Recasens, D., Lamarca, J., Fácil, J.M., Montiel, J., Civera, J., 2021 · 2021
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Self-supervised learning for monocular depth estimation on minimally invasive surgery scenes, in: IEEE International Conference on Robotics and Automation, IEEE. pp. 7159–7165
Shao, S., Pei, Z., Chen, W., Zhang, B., Wu, X., Sun, D., Doermann, D., 2021 · 2021
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The temporal opportunist: self-supervised multi-frame monocular depth, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1164–1174
Watson, J., Mac Aodha, O., Prisacariu, V., Brostow, G., Firman, M., 2021 · 2021
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Monorec: Semi-supervised dense reconstruction in dynamic environments from a single moving camera, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6112–6122
Wimbauer, F., Yang, N., Von Stumberg, L., Zeller, N., Cremers, D., 2021 · 2021
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Semihand: Semi-supervised hand pose estimation with consistency, in: Proceedings of the IEEE International Conference on Computer Vision, pp. 11364–11373
Yang, L., Chen, S., Yao, A., 2021 · 2021
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Self-supervised monocular depth and ego-motion estimation in endoscopy: Appearance flow to the rescue
Shao, S., Pei, Z., Chen, W., Zhu, W., Wu, X., Sun, D., Zhang, B., 2022 · 2022
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Crafting monocular cues and velocity guidance for self-supervised multi-frame depth learning, in: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 2689–2697
Wang, X., Zhu, Z., Huang, G., Chi, X., Ye, Y., Chen, Z., Wang, X., 2023 · 2023
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Self-supervised multi-frame monocular depth estimation for dynamic scenes
Wu, G., Liu, H., Wang, L., Li, K., Guo, Y., Chen, Z., 2023 · 2023
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Exploring the mutual influence between self-supervised single-frame and multi-frame depth estimation
Xiang, J., Wang, Y., An, L., Liu, H., Liu, J., 2023 · 2023
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Multi-view depth estimation by using adaptive point graph to fuse single-view depth probabilities
Wang, K., Liu, C., Liu, Z., Xiao, F., An, Y., Zhao, X., Shen, S., 2024 · 2024
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Spatial transformer networks, in: Advances in Neural Information Processing Systems, pp. 2017–2025
Jaderberg, M., Simonyan, K., Zisserman, A., et al., 2015 · 2025
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