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Depth estimation is a cornerstone of 3D reconstruction and plays a vital role in minimally invasive endoscopic surgeries.
K. He, X. Zhang, S. Ren, and J. Sun, “Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,” in Proceedings of the IEEE international conference on computer vision
2015
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings
2015
Earlier work this paper cites.
T. Zhou, M. Brown, N. Snavely, and D. G. Lowe, “Unsupervised learning of depth and ego-motion from video,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
2017
Earlier work this paper cites.
C. Godard, O. Mac Aodha, and G. J. Brostow, “Unsupervised monocular depth estimation with left-right consistency,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
2017
Earlier work this paper cites.
S. Leonard, A. Sinha, A. Reiter, M. Ishii, G. L. Gallia, R. H. Taylor, and G. D. Hager, “Evaluation and stability analysis of video-based navigation system for functional endoscopic sinus surgery on in vivo clinical data,” IEEE Transactions on Medical Imaging
2018
Earlier work this paper cites.
Y. Chu, X. Li, X. Yang, D. Ai, Y. Huang, H. Song, Y. Jiang, Y. Wang, X. Chen, and J. Yang, “Perception enhancement using importance-driven hybrid rendering for augmented reality based endoscopic surgical navigation,” Biomedical Optics Express
2018
Earlier work this paper cites.
C. Godard, O. Mac Aodha, M. Firman, and G. J. Brostow, “Digging into self-supervised monocular depth estimation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision
2019
Earlier work this paper cites.
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Köpf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, “Pytorch: an imperative style, high-performance deep learning library,” in Proceedings of the 33rd International Conference on Neural Information Processing Systems
2019
Earlier work this paper cites.
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, et al
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
H. Wu, B. Xiao, N. Codella, M. Liu, X. Dai, L. Yuan, and L. Zhang, “Cvt: Introducing convolutions to vision transformers,” in Proceedings of the IEEE/CVF international conference on computer vision
2021
Earlier work this paper cites.
K. B. Ozyoruk, G. I. Gokceler, T. L. Bobrow, G. Coskun, K. Incetan, Y. Almalioglu, F. Mahmood, E. Curto, L. Perdigoto, M. Oliveira, et al
2021
Cited alongside, same era.
A. Aghajanyan, S. Gupta, and L. Zettlemoyer, “Intrinsic dimensionality explains the effectiveness of language model fine-tuning,” in Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing
2021
Cited alongside, same era.
J. Frankle, D. J. Schwab, and A. S. Morcos, “Training batchnorm and only batchnorm: On the expressive power of random features in cnns,” in 9th International Conference on Learning Representations
2021
Cited alongside, same era.
2021
Cited alongside, same era.
A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A. C. Berg, W.-Y. Lo, et al
2023
Later among the works it cites.
D. J. Kopiczko, T. Blankevoort, and Y. M. Asano, “Vera: Vector-based random matrix adaptation,” in 12th International Conference on Learning Representations
2023
Later among the works it cites.
Y. Zheng, C. Zhong, P. Li, H.-a. Gao, Y. Zheng, B. Jin, L. Wang, H. Zhao, G. Zhou, Q. Zhang, and D. Zhao, “Steps: Joint self-supervised nighttime image enhancement and depth estimation,” in 2023 IEEE International Conference on Robotics and Automation (ICRA)
2023
Later among the works it cites.
Q. Zhang, M. Chen, A. Bukharin, P. He, Y. Cheng, W. Chen, and T. Zhao, “Adaptive budget allocation for parameter-efficient fine-tuning,” in The Eleventh International Conference on Learning Representations
2023
Later among the works it cites.
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D. Recasens, J. Lamarca, J. M. Fácil, J. M. M. Montiel, and J. Civera, “Endo-depth-and-motion: Reconstruction and tracking in endoscopic videos using depth networks and photometric constraints,” IEEE Robotics and Automation Letters
2021
Cited alongside, same era.
S. Shao, Z. Pei, W. Chen, W. Zhu, X. Wu, D. Sun, and B. Zhang, “Self-supervised monocular depth and ego-motion estimation in endoscopy: Appearance flow to the rescue,” Medical Image Analysis
2022
Cited alongside, same era.
A. Agarwal and C. Arora, “Depthformer: Multiscale vision transformer for monocular depth estimation with global local information fusion,” in 2022 IEEE International Conference on Image Processing (ICIP)
2022
Cited alongside, same era.
K. Lu, A. Grover, P. Abbeel, and I. Mordatch, “Frozen pretrained transformers as universal computation engines,” in Proceedings of the AAAI conference on artificial intelligence
2022
Cited alongside, same era.
C. Si, W. Yu, P. Zhou, Y. Zhou, X. Wang, and S. Yan, “Inception transformer,” Advances in Neural Information Processing Systems
2022
Cited alongside, same era.
V. M. Batlle, J. Montiel, and J. D. Tardós, “Photometric single-view dense 3d reconstruction in endoscopy,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
2022
Cited alongside, same era.
A. Kolides, A. Nawaz, A. Rathor, D. Beeman, M. Hashmi, S. Fatima, D. Berdik, M. Al-Ayyoub, and Y. Jararweh, “Artificial intelligence foundation and pre-trained models: Fundamentals, applications, opportunities, and social impacts,” Simulation Modelling Practice and Theory
2023
Cited alongside, same era.
Z. Liu, C. Song, J. Cheng, J. Luo, and X. Wang, “Self-supervised monocular depth estimation with effective feature fusion and self distillation,” in 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
2024
Closest in time.
B. Li, B. Liu, M. Zhu, X. Luo, and F. Zhou, “Image intrinsic-based unsupervised monocular depth estimation in endoscopy,” IEEE Journal of Biomedical and Health Informatics
2024
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L. Yang, B. Kang, Z. Huang, X. Xu, J. Feng, and H. Zhao, “Depth anything: Unleashing the power of large-scale unlabeled data,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
2024
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2024
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Z. Fang, Y. Wang, R. Yi, and L. Ma, “Dropout mixture low-rank adaptation for visual parameters-efficient fine-tuning,” in European Conference on Computer Vision
2024
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S. Hayou, N. Ghosh, and B. Yu, “LoRA+: Efficient low rank adaptation of large models,” in Forty-first International Conference on Machine Learning
2024
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