Abstract
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.
Abstract (translated)
全向视频捕获周围环境的整个视场，在虚拟现实应用和遥感等众多领域得到了广泛运用。然而，其广阔的视野往往导致视频中出现不必要的对象。这一问题可以通过视频修复技术（video inpainting）来解决，这种方法可以在保持空间和时间一致性的同时自然地移除这些对象。不过，大多数现有的方法都假定处理的是普通且视场较窄的视频，并未针对全向视频在等距矩形投影中的几何失真进行优化。 为了解决这一问题，本文提出了一种新的深度学习模型，称为“感知畸变的全向视频修复”（Distortion-Aware Omnidirectional Video Inpainting, DAOVI）。DAOVI引入了一个评估图像空间中时间运动信息的模块，并考虑了测地距离。此外，它还设计了一个深度感知特征传播模块，在特征空间内解决全向视频固有的几何失真问题。 实验结果表明，所提出的方法在量化和定性测试上均优于现有方法。
URL
https://arxiv.org/abs/2509.00396
