提出新方法与数据集，解决间接光照下的阴影去除难题。
OmniSR: Shadow Removal under Direct and Indirect Lighting
- 构建新渲染管线生成含影/无影图像对
- 在3万+图像对上训练，效果优于现有方法
- 适合室内室外复杂光照场景的视觉任务
阴影可源自直接和间接光照中的遮挡。尽管当前多数阴影去除研究聚焦于直接光照引起的阴影，但间接光照造成的阴影在室内场景中同样普遍。去除间接光照阴影的一大挑战在于缺乏可用于训练的无影图像。为此，我们提出一种新颖的渲染管道，可在直接和间接光照下生成带影与无影图像，并构建了一个包含超过30,000对图像的综合性合成数据集，涵盖多种物体类型和光照条件。同时，我们设计了一种创新的阴影去除网络，通过拼接与注意力机制显式融合语义与几何先验。实验表明，该方法显著优于现有最优技术，且能有效泛化至各种光照条件下的室内外场景，显著提升了阴影去除方法的整体有效性与适用性。
原文摘要 · Abstract (English)
Shadows can originate from occlusions in both direct and indirect illumination. Although most current shadow removal research focuses on shadows caused by direct illumination, shadows from indirect illumination are often just as pervasive, particularly in indoor scenes. A significant challenge in removing shadows from indirect illumination is obtaining shadow-free images to train the shadow removal network. To overcome this challenge, we propose a novel rendering pipeline for generating shadowed and shadow-free images under direct and indirect illumination, and create a comprehensive synthetic dataset that contains over 30,000 image pairs, covering various object types and lighting conditions. We also propose an innovative shadow removal network that explicitly integrates semantic and geometric priors through concatenation and attention mechanisms. The experiments show that our method outperforms state-of-the-art shadow removal techniques and can effectively generalize to indoor and outdoor scenes under various lighting conditions, enhancing the overall effectiveness and applicability of shadow removal methods.
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。