Unveiling Deep Shadows: A Survey and Benchmark on Image and Video Shadow Detection, Removal, and Generation in the Deep Learning Era

• URL: http://arxiv.org/abs/2409.02108v2
• Date: Mon, 24 Feb 2025 09:23:43 GMT
• Title: Unveiling Deep Shadows: A Survey and Benchmark on Image and Video Shadow Detection, Removal, and Generation in the Deep Learning Era
• Authors: Xiaowei Hu, Zhenghao Xing, Tianyu Wang, Chi-Wing Fu, Pheng-Ann Heng,
• Abstract summary: Shadows are created when light encounters obstacles, resulting in regions of reduced illumination.<n>This paper offers a benchmark on shadow detection, removal, and generation in both images and videos.<n>It focuses on the deep learning approaches of the past decade.
• Score: 81.15890262168449
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Shadows are created when light encounters obstacles, resulting in regions of reduced illumination. In computer vision, detecting, removing, and generating shadows are critical tasks for improving scene understanding, enhancing image quality, ensuring visual consistency in video editing, and optimizing virtual environments. This paper offers a comprehensive survey and evaluation benchmark on shadow detection, removal, and generation in both images and videos, focusing on the deep learning approaches of the past decade. It covers key aspects such as tasks, deep models, datasets, evaluation metrics, and comparative results under consistent experimental settings. Our main contributions include a thorough survey of shadow analysis, the standardization of experimental comparisons, an exploration of the relationships between model size, speed, and performance, a cross-dataset generalization study, the identification of open challenges and future research directions, and the provision of publicly available resources to support further research in this field.

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