Compressed Depth Map Super-Resolution and Restoration: AIM 2024 Challenge Results
원문 초록
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.
함께 살펴볼 논문
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CoReGAN: Contrastive Regularized Generative Adversarial Network for Guided Depth Map Super Resolution
IM-LUT: Interpolation Mixing Look-Up Tables for Image Super-Resolution
PanoDiff-SR: Synthesizing Dental Panoramic Radiographs using Diffusion and Super-resolution
HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation
EAMamba: Efficient All-Around Vision State Space Model for Image Restoration
R3eVision: A Survey on Robust Rendering, Restoration, and Enhancement for 3D Low-Level Vision
토론
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