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Shi et al. Art Int Surg 2024;4:247-57  https://dx.doi.org/10.20517/ais.2024.17                                                               Page 257

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               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].
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