论文标题
没有留下的阴影：使用近似照明和几何形状去除对象及其阴影
No Shadow Left Behind: Removing Objects and their Shadows using Approximate Lighting and Geometry
论文作者
论文摘要
从图像中删除对象是一个具有挑战性的问题，对于包括混合现实在内的许多应用程序都很重要。为了可信的结果，还应删除对象铸件的阴影。当前基于Inpainting的方法仅删除对象本身，将阴影留在后面，或者最多需要指定阴影区域的涂料。我们介绍了一条深度学习的管道，以消除阴影及其施法者。我们利用粗糙的场景模型来从具有多种纹理的表面上删除各种各样的阴影（硬或柔软，黑暗或微妙，大或薄）。我们在合成的数据上训练管道，并在合成场景和真实场景上显示定性和定量结果。
Removing objects from images is a challenging problem that is important for many applications, including mixed reality. For believable results, the shadows that the object casts should also be removed. Current inpainting-based methods only remove the object itself, leaving shadows behind, or at best require specifying shadow regions to inpaint. We introduce a deep learning pipeline for removing a shadow along with its caster. We leverage rough scene models in order to remove a wide variety of shadows (hard or soft, dark or subtle, large or thin) from surfaces with a wide variety of textures. We train our pipeline on synthetically rendered data, and show qualitative and quantitative results on both synthetic and real scenes.
