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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.

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Compressed Depth Map Super-Resolution and Restoration: AIM 2024 Challenge Results

  • 会议论文

  • 发表日期：

    2025年

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  • Marcos V. Conde,
  • Florin-Alexandru Vasluianu,
  • Jinhui Xiong,
  • Wei Ye,
  • Rakesh Ranjan,
  • Radu Timofte,
  • Huan Zheng,
  • Wencheng Han,
  • Tianyi Yan,
  • Jianbing Shen,
  • Pihai Sun,
  • Yuanqi Yao,
  • Kui Jiang,
  • Wenbo Zhao,
  • Xianming Liu,
  • Evgeny Burnaev,
  • Junjun Jiang,
  • Woojae Han,
  • Kyeonghyun Lee,
  • Seongmin Hong,
  • Se Young Chun,
  • Jinseong Kim,
  • Dohyeong Kim,
  • Jeahwan Kim,
  • Yubo Wang,
  • Chi Zhang,
  • Huizhen Luo,
  • Yansai Wu,
  • Mengcheng Huang,
  • Chengji Liu,
  • Chongli Yve,
  • Jianhang Sun,
  • Cheng Guo,
  • Yingcai Du,
  • Huang Jianhao,
  • Liu Shuai,
  • Li Chenghua

摘要

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.