Under the Shadow: Exploiting Opacity Variation for Fine-grained Shadow Detection
Xiaotian Qiao, Ke Xu, Xianglong Yang, Ruijie Dong, Xiaofang Xia, Jiangtao Cui
Shadow characteristics are of great importance for scene understanding.Traditionally, shadow regions are considered as binary masks, often resulting in imprecise detection and suboptimal performance in tasks like shadow removal.We demonstrate that such an assumption oversimplifies light-object interactions in the scene, as the scene details under either hard or soft shadows remain visible to a certain degree.Based on this insight, we aim to reformulate the shadow detection paradigm from the opacity perspective, and introduce a new fine-grained shadow detection method.In particular, given the input image, we first propose a shadow opacity augmentation module to generate realistic images with varied shadow opacities.We then introduce a shadow feature separation module to learn the shadow position and opacity representations separately, followed by an opacity mask prediction module that fuses these representations and predicts fine-grained shadow detection results.In addition, we construct a new dataset with opacity-annotated shadow masks across varied scenarios.Extensive experiments demonstrate that our method outperforms the baselines qualitatively and quantitatively, enhancing downstream shadow applications like shadow removal and editing.The dataset and code will be made publicly available.