Towards Complete Scene Reconstruction from Single-View Depth and Human Motion

Abstract

Complete scene reconstruction from single view RGBD is a challenging task, requiring estimation of scene regions occluded from the captured depth surface. We propose that scene-centric analysis of human motion within an indoor scene can reveal fully occluded objects and provide functional cues to enhance scene understanding tasks. Captured skeletal joint positions of humans, utilised as naturally exploring active sensors, are projected into a human-scene motion representation. Inherent body occupancy is leveraged to carve a volumetric scene occupancy map initialised from captured depth, revealing a more complete voxel representation of the scene. To obtain a structured box model representation of the scene, we introduce unique terms to an object detection optimisation that overcome depth occlusions whilst deriving from the same depth data. The method is evaluated on challenging indoor scenes with multiple occluding objects such as tables and chairs. Evaluation shows that human-centric scene analysis can be applied to effectively enhance state-of-the-art scene understanding approaches, resulting in a more complete representation than single view depth alone.