Compressed Depth mAP Super-Resolution and Restoration: AIM 2024 Challenge Results
Abstract
The increasing demand for augmented reality (AR) and virtual reality (VR) applications highlights the need for efficient depth information processing. Depth maps, essential for rendering realistic scenes and supporting advanced functionalities, are typically large and challenging to stream efficiently due to their size. This challenge introduces a focus on developing innovative depth upsampling techniques to reconstruct high-quality depth maps from compressed data. These techniques are crucial for overcoming the limitations posed by depth compression, which often degrades quality, loses scene details and introduces artifacts. By enhancing depth upsampling methods, this challenge aims to improve the efficiency and quality of depth map reconstruction. Our goal is to advance the state-of-the-art in depth processing technologies, thereby enhancing the overall user experience in AR and VR applications.
Cite
Text
Conde et al. "Compressed Depth mAP Super-Resolution and Restoration: AIM 2024 Challenge Results." European Conference on Computer Vision Workshops, 2024. doi:10.1007/978-3-031-91856-8_17Markdown
[Conde et al. "Compressed Depth mAP Super-Resolution and Restoration: AIM 2024 Challenge Results." European Conference on Computer Vision Workshops, 2024.](https://mlanthology.org/eccvw/2024/conde2024eccvw-compressed/) doi:10.1007/978-3-031-91856-8_17BibTeX
@inproceedings{conde2024eccvw-compressed,
title = {{Compressed Depth mAP Super-Resolution and Restoration: AIM 2024 Challenge Results}},
author = {Conde, Marcos V. and Vasluianu, Florin-Alexandru and Xiong, Jinhui and Ye, Wei and Ranjan, Rakesh and Timofte, Radu and Zheng, Huan and Han, Wencheng and Yan, Tianyi and Shen, Jianbing and Sun, Pihai and Yao, Yuanqi and Jiang, Kui and Zhao, Wenbo and Liu, Xianming and Burnaev, Evgeny and Jiang, Junjun and Han, Woojae and Lee, Kyeonghyun and Hong, Seongmin and Chun, Se Young and Kim, Jinseong and Kim, Dohyeong and Kim, Jeahwan and Wang, Yubo and Zhang, Chi and Luo, Huizhen and Wu, Yansai and Huang, Mengcheng and Liu, Chengji and Yve, Chongli and Sun, Jianhang and Guo, Cheng and Du, Yingcai and Jianhao, Huang and Shuai, Liu and Chenghua, Li},
booktitle = {European Conference on Computer Vision Workshops},
year = {2024},
pages = {287-303},
doi = {10.1007/978-3-031-91856-8_17},
url = {https://mlanthology.org/eccvw/2024/conde2024eccvw-compressed/}
}