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Paper Abstract and Keywords
Presentation 2026-03-16 15:30
High-Efficiency Neural Compression and Real-Time Communication Experiments for LiDAR Point Clouds
Shimizu Hayato, Katto Jiro (Waseda Univ.) IMQ2025-52 IE2025-129 MVE2025-59
Abstract (in Japanese) (See Japanese page) 
(in English) Three-dimensional point cloud data are vital for autonomous driving and VR/AR, but their massive volume necessitates efficient compression. While deep learning-based methods offer hig performance, real-time processing remains a challenge. This study proposes an ancestral feature integration method based on RENO, a real-time neural point cloud codec, which hierarchically integrates features from multiple ancestral scales. By leveraging the broadcast functionality of RENO’s Fast Coordinate Generator, the proposed method supplements contextual information at sparse depths with minimal computational overhead. Experiments on the KITTI Detection dataset show that our method achieves up to a 3.65% bpp reduction over RENO, while limiting processing time increase to approximately 6%, thus maintaining real-time performance. Furthermore, real-time transmission experiments in actual network environments confirm the RENO-based codec’s effectiveness as a practical end-to-end system.
Keyword (in Japanese) (See Japanese page) 
(in English) point cloud compression / deep learning / real-time processing / LiDAR / autonomous driving / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 410, IE2025-129, pp. 188-193, March 2026.
Paper # IE2025-129 
Date of Issue 2026-03-09 (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 IMQ2025-52 IE2025-129 MVE2025-59

Conference Information
Committee CQ MVE IMQ IE  
Conference Date 2026-03-16 - 2026-03-18 
Place (in Japanese) (See Japanese page) 
Place (in English) Okinawa-Sangyoushien-Center 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To IE 
Conference Code 2026-03-CQ-MVE-IMQ-IE 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) High-Efficiency Neural Compression and Real-Time Communication Experiments for LiDAR Point Clouds 
Sub Title (in English)  
Keyword(1) point cloud compression  
Keyword(2) deep learning  
Keyword(3) real-time processing  
Keyword(4) LiDAR  
Keyword(5) autonomous driving  
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1st Author's Name Shimizu Hayato  
1st Author's Affiliation Waseda University (Waseda Univ.)
2nd Author's Name Katto Jiro  
2nd Author's Affiliation Waseda University (Waseda Univ.)
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Speaker Author-1 
Date Time 2026-03-16 15:30:00 
Presentation Time 20 minutes 
Registration for IE 
Paper # IMQ2025-52, IE2025-129, MVE2025-59 
Volume (vol) vol.125 
Number (no) no.408(IMQ), no.410(IE), no.411(MVE) 
Page pp.188-193 
#Pages 6 
Date of Issue 2026-03-09 (IMQ, IE, MVE) 

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