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Paper Abstract and Keywords
Presentation 2021-12-16 10:45
Shadow Reduction Based on LiDAR intensity
Shogo Sato, Yasuhiro Yao, Shingo Ando, Jun Shimamura (NTT) PRMU2021-25
Abstract (in Japanese) (See Japanese page) 
(in English) In recent years, there has been a growing demand
for urban digitization using cameras and light detection and
ranging (LiDAR). LiDAR is ideal for urban measurement independent
from observed conditions. Shadows are a condition
that affects measurement the most. Therefore, shadow detection
and reduction technology is essential. In this study, we propose
shadow detection and reduction utilizing the LiDAR intensity
that depends on the surface properties of objects but not on
irradiation from other light sources. Unlike conventional LiDARintensity-
aided shadow detection methods, our method embeds
the un-correlation between luminance and LiDAR intensity in
each position into the optimization. In our method, LiDAR
intensity is smoothed by a variational method to denoise and
densify. Then, the energy, which is defined by the un-correlation
between luminance and smoothed intensity in each position, is
minimized by graph-cut segmentation to detect shadows. We
also propose a shadow-reduction method based on the shadow
boundary. In evaluations on KITTI and Waymo datasets, our
shadow-detection method outperformed the previous methods in
terms of multiple measurements.
Keyword (in Japanese) (See Japanese page) 
(in English) LiDAR / Shadow detection / Shadow redcution / shadow / / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 304, PRMU2021-25, pp. 7-12, Dec. 2021.
Paper # PRMU2021-25 
Date of Issue 2021-12-09 (PRMU) 
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 PRMU2021-25

Conference Information
Committee PRMU  
Conference Date 2021-12-16 - 2021-12-17 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To PRMU 
Conference Code 2021-12-PRMU 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Shadow Reduction Based on LiDAR intensity 
Sub Title (in English)  
Keyword(1) LiDAR  
Keyword(2) Shadow detection  
Keyword(3) Shadow redcution  
Keyword(4) shadow  
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1st Author's Name Shogo Sato  
1st Author's Affiliation NTT Human Informatics Laboratories (NTT)
2nd Author's Name Yasuhiro Yao  
2nd Author's Affiliation NTT Human Informatics Laboratories (NTT)
3rd Author's Name Shingo Ando  
3rd Author's Affiliation NTT Laboratories (NTT)
4th Author's Name Jun Shimamura  
4th Author's Affiliation NTT Human Informatics Laboratories (NTT)
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Speaker Author-1 
Date Time 2021-12-16 10:45:00 
Presentation Time 15 minutes 
Registration for PRMU 
Paper # PRMU2021-25 
Volume (vol) vol.121 
Number (no) no.304 
Page pp.7-12 
#Pages
Date of Issue 2021-12-09 (PRMU) 


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