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| Presentation | 2023-11-06 10:50 Missing depth compensation based on the characteristics of the RGB-D sensor Tsubasa Murasato, Prima Oky Dicky Ardiansyah (Iwate Prefectural Univ.)  CNR2023-21 HCS2023-83 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | The recent development of a variety of RGB-D cameras has made it possible to generate 3D images for 3D object recognition. However, differences in the positions of the color sensor (RGB) and the depth sensor (D) make it difficult to perfectly match the two images. In particular, the depth information cannot be obtained accurately at the edges of the object or in areas with severe roughness. In this study, RGB-D images with appropriate depth information are created from three RGB-D images with different camera positions, and then a neural network model with an encoder-decoder structure is constructed from these images to compensate for the lack of depth information in the aligned RGB-D images. In the experiment, two cameras were positioned 5 cm to the left and right and 8 cm up and down from the center camera to capture RGB-D images. Finally, the corrected depth information was visually evaluated, and further improvements were considered. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | RGB-D / deep learning, / inpainting / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 123, no. 242, HCS2023-83, pp. 77-81, Nov. 2023. | |
| Paper # | HCS2023-83 | |
| Date of Issue | 2023-10-29 (CNR, HCS) | |
| ISSN | 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 | CNR2023-21 HCS2023-83 |
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| Conference Information | |
| Committee | HCS CNR |
| Conference Date | 2023-11-05 - 2023-11-06 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Kogakuin University |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Communication and Interaction of Human, Robot, and Agent, etc. |
| Paper Information | |
| Registration To | HCS |
| Conference Code | 2023-11-HCS-CNR |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Missing depth compensation based on the characteristics of the RGB-D sensor |
| Sub Title (in English) | |
| Keyword(1) | RGB-D |
| Keyword(2) | deep learning, |
| Keyword(3) | inpainting |
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| 1st Author's Name | Tsubasa Murasato |
| 1st Author's Affiliation | Iwate Prefectural University (Iwate Prefectural Univ.) |
| 2nd Author's Name | Prima Oky Dicky Ardiansyah |
| 2nd Author's Affiliation | Iwate Prefectural University (Iwate Prefectural Univ.) |
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| Speaker | Author-1 |
| Date Time | 2023-11-06 10:50:00 |
| Presentation Time | 25 minutes |
| Registration for | HCS |
| Paper # | CNR2023-21, HCS2023-83 |
| Volume (vol) | vol.123 |
| Number (no) | no.241(CNR), no.242(HCS) |
| Page | pp.77-81 |
| #Pages | 5 |
| Date of Issue | 2023-10-29 (CNR, HCS) |
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