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.
翻译:从图像中移除物体是一个具有挑战性的问题,对于许多应用来说,包括混杂的现实,这是一个非常重要的问题。对于可以相信的结果,物体所投出的阴影也应该被清除。目前基于油漆的方法只是将物体本身移走,留下阴影,或者最多需要指定阴影区域来涂抹。我们引入了一条深层次的学习管道,用其铸造物来清除阴影。我们利用了粗糙的场景模型,以便用多种多样的纹理从表面清除各种各样的阴影(硬的或软的、暗的或微妙的、大的或薄的)。我们用合成的和真实的场景来培训我们的管道,并展示合成的和真实场景的定性和定量结果。