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.

The Second Monocular Depth Estimation Challenge / 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. - ELETTRONICO. - (2023), pp. 3064-3076. (Intervento presentato al convegno IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) tenutosi a Vancouver, Canada nel 17-24 June 2023) [10.1109/cvprw59228.2023.00308].

The Second Monocular Depth Estimation Challenge

Mattoccia, Stefano;Poggi, Matteo;Tosi, Fabio;Zhao, Chaoqiang
2023

Abstract

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.
2023
Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW 2023)
3064
3076
The Second Monocular Depth Estimation Challenge / 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. - ELETTRONICO. - (2023), pp. 3064-3076. (Intervento presentato al convegno IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) tenutosi a Vancouver, Canada nel 17-24 June 2023) [10.1109/cvprw59228.2023.00308].
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
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/961736
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