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Paper Abstract and Keywords
Presentation 2015-02-20 10:40
Model-based Camera Pose Estimation by View Generative Learning
Yukiko Shinozuka, Hideo Saito (Keio Univ.) PRMU2014-141 CNR2014-56
Abstract (in Japanese) (See Japanese page) 
(in English) Augmented Reality (AR) augments a virtual object onto the real-world and gives an enhanced view of the real world. It requires to estimate camera pose to render a virtual object on the target real world position.
However, appearance of the target object may change depending on camera position and pose. The area of highlight on specular object changes by camera pose. This paper proposes a novel method of camera pose estimation for 3D specular object.
There are two main difficulties in this research.
First of all, robust camera pose estimation under viewpoint changes is required. This paper proposes to use View Generative Learning (VGL) framework.
Secondly, robustness to appearance change is necessary. This paper proposes to change rendering method and reference database in VGL. The conventional Lambertian object tracking regards highlight and appearance changes as noise in keypoint matching. On contrary, this paper proposes to take appearance changes as interesting property of the object.
This paper conducted the experiment with CG object and real object to discuss the accuracy and tracking time of the proposed method. Estimated camera pose is compared with ground truth in CG object tracking. This experiment shows the proposed method is more accurate than the conventional machine learning method.
Keyword (in Japanese) (See Japanese page) 
(in English) Camera Pose Estimation / View Generative Learning / / / / / /  
Reference Info. IEICE Tech. Rep., vol. 114, no. 455, CNR2014-56, pp. 137-142, Feb. 2015.
Paper # CNR2014-56 
Date of Issue 2015-02-12 (PRMU, CNR) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
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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 PRMU2014-141 CNR2014-56

Conference Information
Committee PRMU CNR  
Conference Date 2015-02-19 - 2015-02-20 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
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Paper Information
Registration To CNR 
Conference Code 2015-02-PRMU-CNR 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Model-based Camera Pose Estimation by View Generative Learning 
Sub Title (in English)  
Keyword(1) Camera Pose Estimation  
Keyword(2) View Generative Learning  
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1st Author's Name Yukiko Shinozuka  
1st Author's Affiliation Keio University (Keio Univ.)
2nd Author's Name Hideo Saito  
2nd Author's Affiliation Keio University (Keio Univ.)
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Speaker Author-1 
Date Time 2015-02-20 10:40:00 
Presentation Time 30 minutes 
Registration for CNR 
Paper # PRMU2014-141, CNR2014-56 
Volume (vol) vol.114 
Number (no) no.454(PRMU), no.455(CNR) 
Page pp.137-142 
#Pages
Date of Issue 2015-02-12 (PRMU, CNR) 


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