Layout Aware Inpainting for Automated Furniture Removal in Indoor Scenes
This addresses the challenge of realistic furniture removal for interior design applications, but it is incremental as it builds on existing inpainting and perceptual methods.
The paper tackles the problem of removing furniture from indoor scene photos while maintaining geometric consistency in the inpainted background, achieving results that enable redecorating with virtual furniture.
We address the problem of detecting and erasing furniture from a wide angle photograph of a room. Inpainting large regions of an indoor scene often results in geometric inconsistencies of background elements within the inpaint mask. To address this problem, we utilize perceptual information (e.g. instance segmentation, and room layout) to produce a geometrically consistent empty version of a room. We share important details to make this system viable, such as per-plane inpainting, automatic rectification, and texture refinement. We provide detailed ablation along with qualitative examples, justifying our design choices. We show an application of our system by removing real furniture from a room and redecorating it with virtual furniture.