针对全景视频畸变问题，提出新模型实现自然修复。
DAOVI: Distortion-Aware Omnidirectional Video Inpainting
- 引入地心距离感知运动评估模块，处理全景视频时序信息。
- 设计深度感知特征传播模块，缓解等距投影带来的几何畸变。
- 在真实数据上表现更优，适合虚拟现实与遥感应用。
全景视频广泛应用于虚拟现实和遥感等领域，但其大视角常导致不必要物体出现。视频修复技术可自然移除这些物体并保持时空一致性。然而，现有方法多针对窄视角视频，未考虑全景视频等距投影中的畸变问题。本文提出一种新型深度学习模型——畸变感知全景视频修复（DAOVI），引入基于测地线距离的时序运动信息评估模块，以及用于解决全景视频固有几何畸变的深度感知特征传播模块。实验表明，该方法在定量和定性指标上均优于现有方法。
原文摘要 · Abstract (English)
Omnidirectional videos that capture the entire surroundings are employed in a variety of fields such as VR applications and remote sensing. However, their wide field of view often causes unwanted objects to appear in the videos. This problem can be addressed by video inpainting, which enables the natural removal of such objects while preserving both spatial and temporal consistency. Nevertheless, most existing methods assume processing ordinary videos with a narrow field of view and do not tackle the distortion in equirectangular projection of omnidirectional videos. To address this issue, this paper proposes a novel deep learning model for omnidirectional video inpainting, called Distortion-Aware Omnidirectional Video Inpainting (DAOVI). DAOVI introduces a module that evaluates temporal motion information in the image space considering geodesic distance, as well as a depth-aware feature propagation module in the feature space that is designed to address the geometric distortion inherent to omnidirectional videos. The experimental results demonstrate that our proposed method outperforms existing methods both quantitatively and qualitatively.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。