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Removing clutter from scenes is essential in many applications, ranging fromprivacy-concerned content filtering to data augmentation. In this work, wepresent an automatic system that removes clutter from 3D scenes and inpaintswith coherent geometry and texture. We propose techniques for its two keycomponents: 3D segmentation from shared properties and 3D inpainting, both ofwhich are important problems. The definition of 3D scene clutter(frequently-moving objects) is not well captured by commonly-studied objectcategories in computer vision. To tackle the lack of well-defined clutterannotations, we group noisy fine-grained labels, leverage virtual rendering,and impose an instance-level area-sensitive loss. Once clutter is removed, weinpaint geometry and texture in the resulting holes by merging inpainted RGB-Dimages. This requires novel voting and pruning strategies that guaranteemulti-view consistency across individually inpainted images for meshreconstruction. Experiments on ScanNet and Matterport dataset show that ourmethod outperforms baselines for clutter segmentation and 3D inpainting, bothvisually and quantitatively.

Clutter Detection and Removal in 3D Scenes with View-Consistent Inpainting