Abstract
Shadows are formed when light encounters obstacles, leading to areas of diminished illumination. In computer vision, shadow detection, removal, and generation are crucial for enhancing scene understanding, refining image quality, ensuring visual consistency in video editing, and improving virtual environments. This paper presents a comprehensive survey of shadow detection, removal, and generation in images and videos within the deep learning landscape over the past decade, covering tasks, deep models, datasets, and evaluation metrics. Our key contributions include a comprehensive survey of shadow analysis, standardization of experimental comparisons, exploration of the relationships among model size, speed, and performance, a cross-dataset generalization study, identification of open issues and future directions, and provision of publicly available resources to support further research.
Abstract (translated)
影子是在光线遇到障碍物时形成的，导致局部光照不足的区域。在计算机视觉中，影子检测、删除和生成对于增强场景理解、提高图像质量、确保视频编辑中的视觉一致性以及改善虚拟环境至关重要。本文对过去十年中 deep learning 领域图像和视频中的影子检测、删除和生成的全面调查进行了概述，涵盖了任务、深度模型、数据集和评估指标。我们的关键贡献包括对影子的全面分析、实验比较的标准化、模型大小、速度和性能之间的关系探索、跨数据集通用研究、识别出尚未解决的问题和未来研究的方向，以及提供支持进一步研究的公共可用资源。
URL
https://arxiv.org/abs/2409.02108
