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Paper Abstract and Keywords
Presentation 2018-03-19 15:40
3D Indoor Scene Classification using Images Reflecting the Depth Density of Voxel Group
Kazuma Hamada, Masaki Aono (Toyohashi Univ. of Tech.) BioX2017-68 PRMU2017-204
Abstract (in Japanese) (See Japanese page) 
(in English) Along with the spread of VR technology, demand for applications using scenes composed of 3D data is increasing and the number of 3D scene has a trend to increase. If there is technology that can recognize the 3D scene, it will be possible to help classify and organize 3D scenes. In this research, we propose a proprietary imaging method reflecting the depth density of 3D scene converted to voxels and describe indoor 3D scene classification applied to deep learning. By reflecting the depth density of the voxel group from the projection plane with the x, y and z axes as the depths respectively, images useful for classifying the 3D scene is generated. In the experiment, benchmark data sets of six categories were created based on the 3D scene published as Princeton University's SUNCG data set and compared with the conventional method typified by the method using such as voxel group and images as input. As a result, our proposed method could obtain classification result with higher accuracy than the conventional method.
Keyword (in Japanese) (See Japanese page) 
(in English) 3D / 3D Scene / Scene Classification / Voxel / Imaging / Deep Learning / /  
Reference Info. IEICE Tech. Rep., vol. 117, no. 514, PRMU2017-204, pp. 189-194, March 2018.
Paper # PRMU2017-204 
Date of Issue 2018-03-11 (BioX, PRMU) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
Copyright
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reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF BioX2017-68 PRMU2017-204

Conference Information
Committee PRMU BioX  
Conference Date 2018-03-18 - 2018-03-19 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To PRMU 
Conference Code 2018-03-PRMU-BioX 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) 3D Indoor Scene Classification using Images Reflecting the Depth Density of Voxel Group 
Sub Title (in English)  
Keyword(1) 3D  
Keyword(2) 3D Scene  
Keyword(3) Scene Classification  
Keyword(4) Voxel  
Keyword(5) Imaging  
Keyword(6) Deep Learning  
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Keyword(8)  
1st Author's Name Kazuma Hamada  
1st Author's Affiliation Toyohashi University of Technology (Toyohashi Univ. of Tech.)
2nd Author's Name Masaki Aono  
2nd Author's Affiliation Toyohashi University of Technology (Toyohashi Univ. of Tech.)
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Speaker Author-1 
Date Time 2018-03-19 15:40:00 
Presentation Time 25 minutes 
Registration for PRMU 
Paper # BioX2017-68, PRMU2017-204 
Volume (vol) vol.117 
Number (no) no.513(BioX), no.514(PRMU) 
Page pp.189-194 
#Pages 6 
Date of Issue 2018-03-11 (BioX, PRMU) 

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