mlsanityData to 14 Oct 2026

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

arXiv 2409.16277 ↗First posted 24 Sep 2024eess.IV cs.CVcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 27 of its 40 references that are in the map. First posted on arXiv 24 Sep 2024.

Builds on

27 of 40 references.

References · 27

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  2. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
  3. 2023DINOv2: Learning Robust Visual Features without Supervisioncited by 7,974
  4. 2023Adding Conditional Control to Text-to-Image Diffusion Modelscited by 5,503
  5. 2017Arbitrary Style Transfer in Real-time with Adaptive Instance Normalizationcited by 2,010
  6. 2024Depth Anything V2cited by 1,422
  7. 2024Depth Anything: Unleashing the Power of Large-Scale Unlabeled Datacited by 1,360
  8. 2014Depth Map Prediction from a Single Image using a Multi-Scale Deep Networkcited by 1,274
  9. 2023ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depthcited by 657
  10. 2020AdaBins: Depth Estimation using Adaptive Binscited by 601
  11. 2020TartanAir: A Dataset to Push the Limits of Visual SLAMcited by 506
  12. 2021RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matchingcited by 353
15 more
  1. 2018DeepMVS: Learning Multi-view Stereopsiscited by 277
  2. 2020Consistent Video Depth Estimationcited by 220
  3. 2021PENet: Towards Precise and Efficient Image Guided Depth Completioncited by 146
  4. 2019Depth Completion from Sparse LiDAR Data with Depth-Normal Constraintscited by 108
  5. 2022Attention Attention Everywhere: Monocular Depth Prediction with Skip Attentioncited by 93
  6. 2022NAFSSR: Stereo Image Super-Resolution Using NAFNetcited by 76
  7. 2022Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restorationcited by 73
  8. 2023SGNet: Structure Guided Network via Gradient-Frequency Awareness for Depth Map Super-Resolutioncited by 27
  9. 2023Guided Depth Map Super-resolution: A Surveycited by 23
  10. 2024Real-Time 4K Super-Resolution of Compressed AVIF Images. AIS 2024 Challenge Surveycited by 19
  11. 2022Consistent Direct Time-of-Flight Video Depth Super-Resolutioncited by 16
  12. 2024AIM 2024 Challenge on Compressed Video Quality Assessment: Methods and Resultscited by 7
  13. 2024AIM 2024 Challenge on Video Saliency Prediction: Methods and Resultscited by 7
  14. 2024AIM 2024 Sparse Neural Rendering Challenge: Dataset and Benchmarkcited by 6
  15. 2024AIM 2024 Sparse Neural Rendering Challenge: Methods and Resultscited by 4

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Citing papers per year: 2024: 3.

3 papers.