مؤتمر
The Second Monocular Depth Estimation Challenge
| العنوان: | The Second Monocular Depth Estimation Challenge |
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| المؤلفون: | Spencer, Jaime, Qian, C. Stella, Trescakova, Michaela, Russell, Chris, Hadfield, Simon, Graf, Erich W., Adams, Wendy J., Schofield, Andrew J., Elder, James, Bowden, Richard, Anwar, Ali, Chen, Hao, Chen, Xiaozhi, Cheng, Kai, Dai, Yuchao, Hoa, Huynh Thai, Hossain, Sadat, Huang, Jianmian, Jing, Mohan, Li, Bo, Li, Chao, Li, Baojun, Liu, Zhiwen, Mattoccia, Stefano, Mercelis, Siegfried, Nam, Myungwoo, Poggi, Matteo, Qi, Xiaohua, Ren, Jiahui, Tang, Yang, Tosi, Fabio, Trinh, Linh, Uddin, S. M. Nadim, Umair, Khan Muhammad, Wang, Kaixuan, Wang, Yufei, Wang, Yixing, Xiang, Mochu, Xu, Guangkai, Yin, Wei, Yu, Jun, Zhang, Qi, Zhao, Chaoqiang |
| المساهمون: | Spencer, Jaime, Qian, C. Stella, Trescakova, Michaela, Russell, Chri, Hadfield, Simon, Graf, Erich W., Adams, Wendy J., Schofield, Andrew J., Elder, Jame, Bowden, Richard, Anwar, Ali, Chen, Hao, Chen, Xiaozhi, Cheng, Kai, Dai, Yuchao, Hoa, Huynh Thai, Hossain, Sadat, Huang, Jianmian, Jing, Mohan, Li, Bo, Li, Chao, Li, Baojun, Liu, Zhiwen, Mattoccia, Stefano, Mercelis, Siegfried, Nam, Myungwoo, Poggi, Matteo, Qi, Xiaohua, Ren, Jiahui, Tang, Yang, Tosi, Fabio, Trinh, Linh, Uddin, S. M. Nadim, Umair, Khan Muhammad, Wang, Kaixuan, Wang, Yufei, Wang, Yixing, Xiang, Mochu, Xu, Guangkai, Yin, Wei, Yu, Jun, Zhang, Qi, Zhao, Chaoqiang |
| سنة النشر: | 2023 |
| المجموعة: | IRIS Università degli Studi di Bologna (CRIS - Current Research Information System) |
| مصطلحات موضوعية: | depth estimation, deep learning |
| الوصف: | This paper discusses the results for the second edition of the Monocular Depth Estimation Challenge (MDEC). This edition was open to methods using any form of supervision, including fully-supervised, self-supervised, multi-task or proxy depth. The challenge was based around the SYNS-Patches dataset, which features a wide diversity of environments with high-quality dense ground-truth. This includes complex natural environments, e.g. forests or fields, which are greatly underrepresented in current benchmarks.The challenge received eight unique submissions that outperformed the provided SotA baseline on any of the pointcloud- or image-based metrics. The top supervised submission improved relative F-Score by 27.62%, while the top self-supervised improved it by 16.61%. Supervised submissions generally leveraged large collections of datasets to improve data diversity. Self-supervised submissions instead updated the network architecture and pre-trained backbones. These results represent a significant progress in the field, while highlighting avenues for future research, such as reducing interpolation artifacts at depth boundaries, improving self-supervised indoor performance and overall natural image accuracy. |
| نوع الوثيقة: | conference object |
| وصف الملف: | ELETTRONICO |
| اللغة: | English |
| العلاقة: | ispartofbook:Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW 2023); IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); firstpage:3064; lastpage:3076; numberofpages:13; serie:IEEE COMPUTER SOCIETY CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS; https://hdl.handle.net/11585/961736Test; info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85167705538; https://ieeexplore.ieee.org/document/10208890Test |
| DOI: | 10.1109/cvprw59228.2023.00308 |
| الإتاحة: | https://doi.org/10.1109/cvprw59228.2023.00308Test https://hdl.handle.net/11585/961736Test https://ieeexplore.ieee.org/document/10208890Test |
| حقوق: | info:eu-repo/semantics/openAccess |
| رقم الانضمام: | edsbas.6AF58FD2 |
| قاعدة البيانات: | BASE |
| DOI: | 10.1109/cvprw59228.2023.00308 |
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