Classification of LiDAR Point Cloud based on Multiscale Features and PointNet

Author(s):  
Zhao Zhongyang ◽  
Cheng Yinglei ◽  
Shi Xiaosong ◽  
Qin Xianxiang ◽  
Sun Li
Keyword(s):  
Author(s):  
Mathieu Turgeon-Pelchat ◽  
Samuel Foucher ◽  
Yacine Bouroubi

Author(s):  
M. Lemmens

<p><strong>Abstract.</strong> A knowledge-based system exploits the knowledge, which a human expert uses for completing a complex task, through a database containing decision rules, and an inference engine. Already in the early nineties knowledge-based systems have been proposed for automated image classification. Lack of success faded out initial interest and enthusiasm, the same fate neural networks struck at that time. Today the latter enjoy a steady revival. This paper aims at demonstrating that a knowledge-based approach to automated classification of mobile laser scanning point clouds has promising prospects. An initial experiment exploiting only two features, height and reflectance value, resulted in an overall accuracy of 79<span class="thinspace"></span>% for the Paris-rue-Madame point cloud bench mark data set.</p>


Author(s):  
J. Shao ◽  
W. Zhang ◽  
Y. Zhu ◽  
A. Shen

Image has rich color information, and it can help to promote recognition and classification of point cloud. The registration is an important step in the application of image and point cloud. In order to give the rich texture and color information for LiDAR point cloud, the paper researched a fast registration method of point cloud and sequence images based on the ground-based LiDAR system. First, calculating transformation matrix of one of sequence images based on 2D image and LiDAR point cloud; second, using the relationships of position and attitude information among multi-angle sequence images to calculate all transformation matrixes in the horizontal direction; last, completing the registration of point cloud and sequence images based on the collinear condition of image point, projective center and LiDAR point. The experimental results show that the method is simple and fast, and the stitching error between adjacent images is litter; meanwhile, the overall registration accuracy is high, and the method can be used in engineering application.


2018 ◽  
Vol 45 (10) ◽  
pp. 1004001
Author(s):  
佟国峰 Tong Guofeng ◽  
杜宪策 Du Xiance ◽  
李勇 Li Yong ◽  
陈槐嵘 Chen Huairong ◽  
张庆春 Zhang Qingchun

2019 ◽  
Vol 56 (5) ◽  
pp. 052804
Author(s):  
赵中阳 Zhao Zhongyang ◽  
程英蕾 Cheng Yinglei ◽  
释小松 Shi Xiaosong ◽  
秦先祥 Qin Xianxiang ◽  
李鑫 Li Xin

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