Page 103 - Read Online
P. 103
Shi et al. Art Int Surg 2024;4:247-57 https://dx.doi.org/10.20517/ais.2024.17 Page 257
Conflicts of interest
All authors declared that there are no conflicts of interest.
Ethical approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Copyright
© The Author(s) 2024.
REFERENCES
1. Godard C, Mac Aodha O, Firman M, Brostow G. Digging into self-supervised monocular depth estimation. In: 2019 IEEE/CVF
International Conference on Computer Vision (ICCV); 2019 Oct 27 - Nov 02; Seoul, Korea. IEEE; 2019. pp. 3827-37. DOI
2. Godard C, Mac Aodha O, Brostow GJ. Unsupervised monocular depth estimation with left-right consistency. In: 2017 IEEE
Conference on Computer Vision and Pattern Recognition (CVPR); 2017 Jul 21-26; Honolulu, USA. IEEE; 2017. pp. 6602-11. DOI
3. Lyu X, Liu L, Wang M, et al. Hr-depth: high resolution self-supervised monocular depth estimation. AAAI Conf Artif Intell
2021;35:2294301. DOI
4. Garg R, Bg VK, Carneiro G, Reid I. Unsupervised CNN for single view depth estimation: Geometry to the rescue. In: Computer
Vision - ECCV 2016: 14th European Conference; 2016 Oct 11-14; Amsterdam, the Netherlands. Springer; 2016. pp. 740-56. DOI
5. Jaderberg M, Simonyan K, Zisserman A, Kavukcuoglu K. Spatial transformer networks. 2015. Available from: https://proceedings.
neurips.cc/paper_files/paper/2015/file/33ceb07bf4eeb3da587e268d663aba1a-Paper.pdf. [Last accessed on 5 Sep 2024]
6. Wang Z, Simoncelli EP, Bovik AC. Multiscale structural similarity for image quality assessment. In: The Thrity-Seventh Asilomar
Conference on Signals, Systems & Computers, 2003; 2023 Nov 09-12; Pacific Grove, USA. IEEE; 2003. pp. 1398-402. DOI
7. Zhou T, Brown M, Snavely N, Lowe DG. Unsupervised learning of depth and ego-motion from video. In: 2017 IEEE Conference on
Computer Vision and Pattern Recognition (CVPR); 2017 Jul 21-26; Honululu, USA. IEEE; 2017. pp. 6612-9. DOI
8. Ranjan A, Jampani V, Balles L, et al. Competitive collaboration: joint unsupervised learning of depth, camera motion, optical flow and
motion segmentation. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2019 Jun 15-20; Long
Beach, USA. IEEE; 2019. pp. 12232-41. DOI
9. Shao S, Pei Z, Chen W, et al. Self-supervised monocular depth and ego-motion estimation in endoscopy: appearance flow to the
rescue. Med Image Anal 2022;77:102338. DOI
10. Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation. In: Medical Image Computing
and Computer-Assisted Intervention - MICCAI 2015: 18th International Conference; 2015 Oct 5-9; Munich, Germany. Springer; 2015.
pp. 234-41. DOI
11. He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and
Pattern Recognition (CVPR); 2016 Jun 27-30; Las Vegas, USA. IEEE; 2016. pp. 770-8. DOI
12. Allan M, Mcleod J, Wang C, et al. Stereo correspondence and reconstruction of endoscopic data challenge. arXiv. [Preprint.] Jan 28,
2021 [accessed on 2024 Sep 5]. Available from: https://doi.org/10.48550/arXiv.2101.01133.
13. Shao S, Pei Z, Chen W, et al. Self-supervised learning for monocular depth estimation on minimally invasive surgery scenes. In: 2021
IEEE International Conference on Robotics and Automation (ICRA); 2021 May 30 - Jun 05; Xi’an, China. IEEE; 2021. pp. 7159-65.
DOI
14. Recasens D, Lamarca J, Fácil JM, Montiel JMM, Civera J. Endo-depth-and-motion: reconstruction and tracking in endoscopic videos
using depth networks and photometric constraints. IEEE Robot Autom Lett 2021;6:7225-32. DOI
15. Li W, Hayashi Y, Oda M, Kitasaka T, Misawa K, Mori K. Context encoder guided self-supervised siamese depth estimation based on
stereo laparoscopic images. In: Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling. 2021. pp. 77-
82. DOI
16. Zhang N, Nex F, Vosselman G, Kerle N. Lite-mono: a lightweight CNN and transformer architecture for self-supervised monocular
depth estimation. In: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2023 Jun 17-24; Vancouver,
Canada. IEEE; 2023. pp. 18537-46. DOI
17. Zhao C, Zhang Y, Poggi M, et al. Monovit: self-supervised monocular depth estimation with a vision transformer. In: 2022
International Conference on 3D Vision (3DV); 2022 Sep 12-16; Prague, Czech Pepublic. IEEE; 2022. pp. 668-78. DOI
18. Yang L, Kang B, Huang Z, Xu X, Feng J, Zhao H. Depth anything: unleashing the power of large-scale unlabeled data. In:
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2024. pp. 10371-81. Available from:
https://openaccess.thecvf.com/content/CVPR2024/html/Yang_Depth_Anything_Unleashing_the_Power_of_Large-Scale_Unlabeled_
Data_CVPR_2024_paper.html. [Last accessed on 5 Sep 2024].

