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| Presentation | 2024-03-15 10:30 A Point Cloud Downsampling Method Considering Local Features for 3D Object Classification Ryota Sugimoto, Kenji Kanai, Shiori Maki, Jiro Katto (Waseda Univ.)  IMQ2023-70 IE2023-125 MVE2023-99 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | In recent years, use of point cloud data has been considered for 3D spatial recognition. To reduce processing load caused by the amount of point cloud data, down-sampling is often applied as a pre-processing step. However, most of the existing methods do not take care of subsequent stages of point cloud processing and final accuracy degradation. Some methods consider the subsequent processing and prioritize preservation of the overall geometry, but the loss of local geometry sometimes causes accuracy degradation. In this study, we therefore propose a down-sampling method that suppresses both processing time and accuracy degradation by using class classification process of 3D point clouds as a post-processing step. Specifically, we evaluate the performance of a method in which the point density of local regions is increased for the training point cloud of SampleNet, a deep learning-based downsampling method. The proposed method improves the processing load and classification accuracy by considering local features. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Point Cloud / Downsampling / Deep Learning / Local Features / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 123, no. 432, IE2023-125, pp. 313-318, March 2024. | |
| Paper # | IE2023-125 | |
| Date of Issue | 2024-03-06 (IMQ, IE, MVE) | |
| 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 | IMQ2023-70 IE2023-125 MVE2023-99 |
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| Conference Information | |
| Committee | IE MVE CQ IMQ |
| Conference Date | 2024-03-13 - 2024-03-15 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Okinawa Sangyo Shien Center |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Media of five senses, Multimedia, Media experience, Picture codinge, Image media quality, Network,quality and reliability, etc（AC） |
| Paper Information | |
| Registration To | IE |
| Conference Code | 2024-03-IE-MVE-CQ-IMQ |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | A Point Cloud Downsampling Method Considering Local Features for 3D Object Classification |
| Sub Title (in English) | |
| Keyword(1) | Point Cloud |
| Keyword(2) | Downsampling |
| Keyword(3) | Deep Learning |
| Keyword(4) | Local Features |
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| 1st Author's Name | Ryota Sugimoto |
| 1st Author's Affiliation | Waseda University (Waseda Univ.) |
| 2nd Author's Name | Kenji Kanai |
| 2nd Author's Affiliation | Waseda University (Waseda Univ.) |
| 3rd Author's Name | Shiori Maki |
| 3rd Author's Affiliation | Waseda University (Waseda Univ.) |
| 4th Author's Name | Jiro Katto |
| 4th Author's Affiliation | Waseda University (Waseda Univ.) |
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| Speaker | Author-1 |
| Date Time | 2024-03-15 10:30:00 |
| Presentation Time | 20 minutes |
| Registration for | IE |
| Paper # | IMQ2023-70, IE2023-125, MVE2023-99 |
| Volume (vol) | vol.123 |
| Number (no) | no.430(IMQ), no.432(IE), no.433(MVE) |
| Page | pp.313-318 |
| #Pages | 6 |
| Date of Issue | 2024-03-06 (IMQ, IE, MVE) |
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