No Shadow Left Behind: Removing Objects and their Shadows using Approximate Lighting and Geometry

PubDate: Dec 2020

Teams: University of Washington 2Google

Writers: Edward Zhang, Ricardo Martin-Brualla, Janne Kontkanen, Brian Curless

PDF: No Shadow Left Behind: Removing Objects and their Shadows using Approximate Lighting and Geometry

Abstract

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.