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

arXiv: https://arxiv.org/abs/2409.16277 · HTML: https://mlsanity.com/paper/2409.16277/

## 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 (most cited first, 27 of 27)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [Depth Anything V2](https://mlsanity.com/paper/2406.09414/) (2024)
- [Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data](https://mlsanity.com/paper/2401.10891/) (2024)
- [Depth Map Prediction from a Single Image using a Multi-Scale Deep Network](https://mlsanity.com/paper/1406.2283/) (2014)
- [ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth](https://mlsanity.com/paper/2302.12288/) (2023)
- [AdaBins: Depth Estimation using Adaptive Bins](https://mlsanity.com/paper/2011.14141/) (2020)
- [TartanAir: A Dataset to Push the Limits of Visual SLAM](https://mlsanity.com/paper/2003.14338/) (2020)
- [RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching](https://mlsanity.com/paper/2109.07547/) (2021)
- [DeepMVS: Learning Multi-view Stereopsis](https://mlsanity.com/paper/1804.00650/) (2018)
- [Consistent Video Depth Estimation](https://mlsanity.com/paper/2004.15021/) (2020)
- [PENet: Towards Precise and Efficient Image Guided Depth Completion](https://mlsanity.com/paper/2103.00783/) (2021)
- [Depth Completion from Sparse LiDAR Data with Depth-Normal Constraints](https://mlsanity.com/paper/1910.06727/) (2019)
- [Attention Attention Everywhere: Monocular Depth Prediction with Skip Attention](https://mlsanity.com/paper/2210.09071/) (2022)
- [NAFSSR: Stereo Image Super-Resolution Using NAFNet](https://mlsanity.com/paper/2204.08714/) (2022)
- [Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration](https://mlsanity.com/paper/2209.11345/) (2022)
- [SGNet: Structure Guided Network via Gradient-Frequency Awareness for Depth Map Super-Resolution](https://mlsanity.com/paper/2312.05799/) (2023)
- [Guided Depth Map Super-resolution: A Survey](https://mlsanity.com/paper/2302.09598/) (2023)
- [Real-Time 4K Super-Resolution of Compressed AVIF Images. AIS 2024 Challenge Survey](https://mlsanity.com/paper/2404.16484/) (2024)
- [Consistent Direct Time-of-Flight Video Depth Super-Resolution](https://mlsanity.com/paper/2211.08658/) (2022)
- [AIM 2024 Challenge on Compressed Video Quality Assessment: Methods and Results](https://mlsanity.com/paper/2408.11982/) (2024)
- [AIM 2024 Challenge on Video Saliency Prediction: Methods and Results](https://mlsanity.com/paper/2409.14827/) (2024)
- [AIM 2024 Sparse Neural Rendering Challenge: Dataset and Benchmark](https://mlsanity.com/paper/2409.15041/) (2024)
- [AIM 2024 Sparse Neural Rendering Challenge: Methods and Results](https://mlsanity.com/paper/2409.15045/) (2024)

## Built on by (most cited first, 3 of 3)

- [AIM 2024 Challenge on Efficient Video Super-Resolution for AV1 Compressed Content](https://mlsanity.com/paper/2409.17256/) (2024)
- [Decoupling Fine Detail and Global Geometry for Compressed Depth Map Super-Resolution](https://mlsanity.com/paper/2411.03239/) (2024)
- [AIM 2024 Challenge on Video Super-Resolution Quality Assessment: Methods and Results](https://mlsanity.com/paper/2410.04225/) (2024)

## 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.