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

Fowler, Samuel, Kim, Hansung and Hilton, Adrian (2017) Towards Complete Scene Reconstruction from Single-View Depth and Human Motion In: 28th British Machine Vision Conference (BMVC 2017), 04-07 Sep 2017, London, UK.

Towards Complete Scene Reconstruction from Single-View Depth and Human Motion.pdf - Accepted version Manuscript

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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.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
Date : 2017
Copyright Disclaimer : © 2017. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.
Depositing User : Clive Harris
Date Deposited : 19 Jul 2017 09:04
Last Modified : 25 Jun 2018 12:54

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