scholarly journals 3D Modeling of Urban Facility Using Point Cloud Data for Realization of Smart City

2020 ◽  
Vol 32 (11) ◽  
pp. 3853
Author(s):  
Joon Kyu Park ◽  
Keun Wang Lee
Keyword(s):  
3D Modeling ◽  
Smart City ◽  
Point Cloud ◽  
Point Cloud Data ◽  
Cloud Data

3D Modeling Approach of Building Construction based on Point Cloud Data Using LiDAR

2019 ◽  
Author(s):  
Byeongjun Oh ◽  
Minju Kim ◽  
Chanwoo Lee ◽  
Hunhee Cho ◽  
Kyung-In Kang
Keyword(s):  
3D Modeling ◽  
Point Cloud ◽  
Building Construction ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Modeling Approach

scholarly journals LIDAR Point Cloud Data Extraction and Establishment of 3D Modeling of Buildings

2018 ◽  
Vol 301 ◽  
pp. 012037
Author(s):  
Yujuan Zhang ◽  
Xiuhai Li ◽  
Qiang Wang ◽  
Jiang Liu ◽  
Xin Liang ◽  
...  
Keyword(s):  
3D Modeling ◽  
Point Cloud ◽  
Data Extraction ◽  
Point Cloud Data ◽  
Cloud Data

Research on Trunk Modeling Based on 3D Laser Scanning

2011 ◽  
Vol 467-469 ◽  
pp. 1674-1679
Author(s):  
Lu Lu Wu ◽  
Ying Chen ◽  
Zhong Ke Feng ◽  
Xue Hai Tang ◽  
Zhuo Xu ◽  
...  
Keyword(s):  
3D Modeling ◽  
Point Cloud ◽  
Laser Scanning ◽  
Point Cloud Data ◽  
3D Laser Scanning ◽  
Cloud Data ◽  
Maximal Distance ◽  
Encapsulation Methods

This Point cloud data of trees extracted from 3D laser scanning were used to do analysis and research on the 3D modeling of trunks. The software of Geomagic Studio was used to separate and eliminate the noises of point cloud of trees, to acquire the point cloud data of trunks. On the basis of this, the data was resampled and modeled by different encapsulation methods. The result demonstrates that the model got by the method of maximal distance is the best.

Registration and Fusion of UAV LiDAR System Sequence Images and Laser Point Clouds

2020 ◽  
Author(s):  
Jiayong Yu ◽  
Longchen Ma ◽  
Maoyi Tian, ◽  
Xiushan Lu
Keyword(s):  
Point Cloud ◽  
Image Data ◽  
Point Clouds ◽  
Optical Image ◽  
Point Cloud Data ◽  
Feature Points ◽  
Lidar System ◽  
Registration Accuracy ◽  
Cloud Data ◽  
Intensity Image

The unmanned aerial vehicle (UAV)-mounted mobile LiDAR system (ULS) is widely used for geomatics owing to its efficient data acquisition and convenient operation. However, due to limited carrying capacity of a UAV, sensors integrated in the ULS should be small and lightweight, which results in decrease in the density of the collected scanning points. This affects registration between image data and point cloud data. To address this issue, the authors propose a method for registering and fusing ULS sequence images and laser point clouds, wherein they convert the problem of registering point cloud data and image data into a problem of matching feature points between the two images. First, a point cloud is selected to produce an intensity image. Subsequently, the corresponding feature points of the intensity image and the optical image are matched, and exterior orientation parameters are solved using a collinear equation based on image position and orientation. Finally, the sequence images are fused with the laser point cloud, based on the Global Navigation Satellite System (GNSS) time index of the optical image, to generate a true color point cloud. The experimental results show the higher registration accuracy and fusion speed of the proposed method, thereby demonstrating its accuracy and effectiveness.

REPRESENTATION AND MODELING OF THREE-DIMENSIONAL LANDFORM DATA BASED ON POINT CLOUD DATA USING VARIOUS MEASUREMENT HARDWARE

2018 ◽  
Vol 74 (2) ◽  
pp. II_99-II_109 ◽  
Author(s):  
Satoshi KUBOTA ◽  
Ryuichi IMAI ◽  
Kenji NAKAMURA ◽  
Jun SAKURAI ◽  
Shigenori TANAKA
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Point Cloud Data ◽  
Cloud Data
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