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| Presentation | 2016-06-13 11:15 Preliminary study on deep manifold embedding for 3D object pose estimation Hiroshi Ninomiya, Yasutomo Kawanishi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase (Nagoya Univ.), Norimasa Kobori, Kunimatsu Hashimoto (Toyota)  PRMU2016-39 SP2016-5 WIT2016-5 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Recently, 3D object pose estimation is being focused. The parametric eigenspace method is known as one of the fundamental methods. It represents the appearance change of an object caused by pose change with a manifold embedded in a low-dimentional subspace. It obtains features by PCA, which maximizes the appearance variation. However, there is not always a correlation between pose change and appearance change. So, there is a problem that the method cannot handle a pose change with a slight appearance change. In this report, we introduce deep manifold embedding which maximizes the pose variation. We construct a manifold from features extracted from Deep Convolutional Neural Networks (DCNNs) trained with pose information. Pose estimation with the proposed method achieved the best accuracy in experiments using a public dataset. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | 3D object / pose estimation / manifold / deep learning / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 116, no. 89, PRMU2016-39, pp. 25-30, June 2016. | |
| Paper # | PRMU2016-39 | |
| Date of Issue | 2016-06-06 (PRMU, SP, WIT) | |
| 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 | PRMU2016-39 SP2016-5 WIT2016-5 |
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| Conference Information | |
| Committee | PRMU SP WIT ASJ-H |
| Conference Date | 2016-06-13 - 2016-06-14 |
| 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 | 2016-06-PRMU-SP-WIT-H |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Preliminary study on deep manifold embedding for 3D object pose estimation |
| Sub Title (in English) | |
| Keyword(1) | 3D object |
| Keyword(2) | pose estimation |
| Keyword(3) | manifold |
| Keyword(4) | deep learning |
| Keyword(5) | |
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| Keyword(8) | |
| 1st Author's Name | Hiroshi Ninomiya |
| 1st Author's Affiliation | Nagoya University (Nagoya Univ.) |
| 2nd Author's Name | Yasutomo Kawanishi |
| 2nd Author's Affiliation | Nagoya University (Nagoya Univ.) |
| 3rd Author's Name | Daisuke Deguchi |
| 3rd Author's Affiliation | Nagoya University (Nagoya Univ.) |
| 4th Author's Name | Ichiro Ide |
| 4th Author's Affiliation | Nagoya University (Nagoya Univ.) |
| 5th Author's Name | Hiroshi Murase |
| 5th Author's Affiliation | Nagoya University (Nagoya Univ.) |
| 6th Author's Name | Norimasa Kobori |
| 6th Author's Affiliation | Toyota Motor Corporation (Toyota) |
| 7th Author's Name | Kunimatsu Hashimoto |
| 7th Author's Affiliation | Toyota Motor Corporation (Toyota) |
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| Speaker | Author-1 |
| Date Time | 2016-06-13 11:15:00 |
| Presentation Time | 30 minutes |
| Registration for | PRMU |
| Paper # | PRMU2016-39, SP2016-5, WIT2016-5 |
| Volume (vol) | vol.116 |
| Number (no) | no.89(PRMU), no.90(SP), no.91(WIT) |
| Page | pp.25-30 |
| #Pages | 6 |
| Date of Issue | 2016-06-06 (PRMU, SP, WIT) |
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