Compressed Depth Map Super-Resolution and Restoration: AIM 2024 Challenge Results
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| Format: | Preprint |
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2024
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| author | Conde, Marcos V. Vasluianu, Florin-Alexandru Xiong, Jinhui Ye, Wei Ranjan, Rakesh Timofte, Radu |
| author_facet | Conde, Marcos V. Vasluianu, Florin-Alexandru Xiong, Jinhui Ye, Wei Ranjan, Rakesh Timofte, Radu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_16277 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Compressed Depth Map Super-Resolution and Restoration: AIM 2024 Challenge Results Conde, Marcos V. Vasluianu, Florin-Alexandru Xiong, Jinhui Ye, Wei Ranjan, Rakesh Timofte, Radu Image and Video Processing Computer Vision and Pattern Recognition 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. |
| title | Compressed Depth Map Super-Resolution and Restoration: AIM 2024 Challenge Results |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2409.16277 |