Paper Abstract and Keywords |
Presentation |
2017-03-21 14:10
Three-dimensional shape retrieval based in voxel representation using LSTM and CNN Ryo Miyagi, Masaki Aono (TUT) BioX2016-67 PRMU2016-230 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
In recent years, with the spread of 3D scanners, VR headsets, etc., there is an increasing demand for recognition and retrieval for 3D contents. Furthermore, by applying deep learning to 3-dimensional objects, research on shape classification, shape search and the like are actively conducted. In this paper, we represent our model with binary voxels and propose a new method for searching similar 3D shape models using deep learning. Specifically, we employ a Convolutional Neural Network (CNN) to represent a 2D slice extracting from 3D binary voxels, and a Long Short-Term Memory (LSTM) to represent 2D slices as time-series connected features showing a given 3D shape. As a result of the experiment, we could improve the performance of our method against a baseline method which is based on 3DCNN. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Deep Learning / 3D / LSTM / CNN / Shape Retrieval / Binary Voxel / / |
Reference Info. |
IEICE Tech. Rep., vol. 116, no. 528, PRMU2016-230, pp. 203-208, March 2017. |
Paper # |
PRMU2016-230 |
Date of Issue |
2017-03-13 (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) |
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BioX2016-67 PRMU2016-230 |
Conference Information |
Committee |
PRMU BioX |
Conference Date |
2017-03-20 - 2017-03-21 |
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(See Japanese page) |
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Paper Information |
Registration To |
PRMU |
Conference Code |
2017-03-PRMU-BioX |
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Japanese |
Title (in Japanese) |
(See Japanese page) |
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(See Japanese page) |
Title (in English) |
Three-dimensional shape retrieval based in voxel representation using LSTM and CNN |
Sub Title (in English) |
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Deep Learning |
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3D |
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LSTM |
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CNN |
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Shape Retrieval |
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Binary Voxel |
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1st Author's Name |
Ryo Miyagi |
1st Author's Affiliation |
Toyohashi University of Technology (TUT) |
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Masaki Aono |
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Toyohashi University of Technology (TUT) |
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Speaker |
Author-1 |
Date Time |
2017-03-21 14:10:00 |
Presentation Time |
25 minutes |
Registration for |
PRMU |
Paper # |
BioX2016-67, PRMU2016-230 |
Volume (vol) |
vol.116 |
Number (no) |
no.527(BioX), no.528(PRMU) |
Page |
pp.203-208 |
#Pages |
6 |
Date of Issue |
2017-03-13 (BioX, PRMU) |
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