Details

Autor(en) / Beteiligte

• Sheng, Hao
• Liu, Yebin
• Yu, Jingyi
• Wu, Gaochang
• Xiong, Wei
• Cong, Ruixuan
• Chen, Rongshan
• Guo, Longzhao
• Xie, Yanlin
• Zhang, Shuo
• Chang, Song
• Lin, Youfang
• Chao, Wentao
• Wang, Xuechun
• Wang, Guanghui
• Duan, Fuqing
• Wang, Tun
• Yang, Da
• Cui, Zhenglong
• Wang, Sizhe
• Zhao, Mingyuan
• Wang, Qiong
• Chen, Qianyu
• Liang, Zhengyu
• Wang, Yingqian
• Yang, Jungang
• Yang, Xueting
• Deng, Junli

Titel

LFNAT 2023 Challenge on Light Field Depth Estimation: Methods and Results

Ist Teil von

• 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2023, p.3473-3485

Ort / Verlag

IEEE

Erscheinungsjahr

2023

Link zum Volltext

• IEEE_Xplore

Quelle

IEEE Xplore Digital Library

Beschreibungen/Notizen

• This paper reviews the 1st LFNAT challenge on light field depth estimation, which aims at predicting disparity information of central view image in a light field (i.e., pixel offset between central view image and adjacent view image). Compared to multi-view stereo matching, light field depth estimation emphasizes efficient utilization of the 2D angular information from multiple regularly varying views. This challenge specifies UrbanLF [20] light field dataset as the sole data source. There are two phases in total: submission phase and final evaluation phase, in which 75 registered participants successfully submit their predicted results in the first phase and 7 eligible teams compete in the second phase. The performance of all submissions is carefully reviewed and shown in this paper as a new standard for the current state-of-the-art in light field depth estimation. Moreover, the implementation details of these methods are also provided to stimulate related advanced research.