Food volume estimation based on deep learning view synthesis from a single depth map
File(s)
Published version
Author(s)
Lo, Frank P-W
Sun, Yingnan
Qiu, Jianing
Lo, Benny
Type
Journal Article
Abstract
An objective dietary assessment system can help users to understand their dietary behavior and enable targeted interventions to address underlying health problems. To accurately quantify dietary intake, measurement of the portion size or food volume is required. For volume estimation, previous research studies mostly focused on using model-based or stereo-based approaches which rely on manual intervention or require users to capture multiple frames from different viewing angles which can be tedious. In this paper, a view synthesis approach based on deep learning is proposed to reconstruct 3D point clouds of food items and estimate the volume from a single depth image. A distinct neural network is designed to use a depth image from one viewing angle to predict another depth image captured from the corresponding opposite viewing angle. The whole 3D point cloud map is then reconstructed by fusing the initial data points with the synthesized points of the object items through the proposed point cloud completion and Iterative Closest Point (ICP) algorithms. Furthermore, a database with depth images of food object items captured from different viewing angles is constructed with image rendering and used to validate the proposed neural network. The methodology is then evaluated by comparing the volume estimated by the synthesized 3D point cloud with the ground truth volume of the object items
Date Issued
2018-12-18
Date Acceptance
2018-12-16
Citation
Nutrients, 2018, 10 (12), pp.1-20
ISSN
2072-6643
Publisher
MDPI AG
Start Page
1
End Page
20
Journal / Book Title
Nutrients
Volume
10
Issue
12
Copyright Statement
© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/)
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/)
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000455073200186&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Subjects
Science & Technology
Life Sciences & Biomedicine
Nutrition & Dietetics
dietary assessment
volume estimation
mhealth
deep learning
view synthesis
image rendering
3d reconstruction
3D
RECONSTRUCTION
Publication Status
Published
Article Number
ARTN 2005
Date Publish Online
2018-12-18