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Towards complete scene reconstruction from single-view depth and human motion

Towards complete scene reconstruction from single-view depth and human motion
Towards complete scene reconstruction from single-view depth and human motion
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. © 2017. The copyright of this document resides with its authors.
Object detection, Motion representation, Occluded objects, Scene analysis, Scene reconstruction, Scene understanding, Skeletal joints, State of the art, Voxel representation, Computer vision
Fowler, S.
a4d9db52-325d-44ba-84e9-64db8cbc029e
Kim, H.
2c7c135c-f00b-4409-acb2-85b3a9e8225f
Hilton, A.
12782a55-4c4d-4dfb-a690-62505f6665db
Fowler, S.
a4d9db52-325d-44ba-84e9-64db8cbc029e
Kim, H.
2c7c135c-f00b-4409-acb2-85b3a9e8225f
Hilton, A.
12782a55-4c4d-4dfb-a690-62505f6665db

Fowler, S., Kim, H. and Hilton, A. (2017) Towards complete scene reconstruction from single-view depth and human motion. British Machine Vision Conference, United Kingdom. 04 - 07 Sep 2017.

Record type: Conference or Workshop Item (Paper)

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. © 2017. The copyright of this document resides with its authors.

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More information

Published date: 2017
Venue - Dates: British Machine Vision Conference, United Kingdom, 2017-09-04 - 2017-09-07
Keywords: Object detection, Motion representation, Occluded objects, Scene analysis, Scene reconstruction, Scene understanding, Skeletal joints, State of the art, Voxel representation, Computer vision

Identifiers

Local EPrints ID: 440600
URI: http://eprints.soton.ac.uk/id/eprint/440600
PURE UUID: 343d4527-6d1c-467b-8f87-ac570dcff68e
ORCID for H. Kim: ORCID iD orcid.org/0000-0003-4907-0491

Catalogue record

Date deposited: 12 May 2020 16:30
Last modified: 22 Aug 2020 01:52

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