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| 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 |
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| 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 and 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 |
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| 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 |
| Keyword(7) | |
| 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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