RESEARCH ON AUTOMATIC EXTRACTION OF BLOCK WALL USING POINT CLOUD DATA

2021 ◽  
Vol 77 (2) ◽  
pp. I_161-I_173
Author(s):  
Yoshimasa UMEHARA ◽  
Yoshinori TSUKADA ◽  
Shigenori TANAKA ◽  
Yasunori KOZUKI ◽  
Tsuneaki SHIMONARU ◽  
...  
Keyword(s):  
Point Cloud ◽  
Automatic Extraction ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Block Wall

scholarly journals Automatic extraction of pavement markings on streets from point cloud data of mobile LiDAR

2017 ◽  
Vol 28 (8) ◽  
pp. 085203 ◽  
Author(s):  
Yang Gao ◽  
Ruofei Zhong ◽  
Tao Tang ◽  
Liuzhao Wang ◽  
Xianlin Liu
Keyword(s):  
Point Cloud ◽  
Automatic Extraction ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Pavement Markings

scholarly journals AUTOMATIC EXTRACTION AND TOPOLOGY RECONSTRUCTION OF URBAN VIADUCTS FROM LIDAR DATA

2015 ◽  
Vol XL-3/W3 ◽  
pp. 131-135
Author(s):  
Y. Wang ◽  
X. Hu
Keyword(s):  
Point Cloud ◽  
Original Method ◽  
Airborne Lidar ◽  
Least Square ◽  
Automatic Extraction ◽  
Point Cloud Data ◽  
Topological Graph ◽  
Cloud Data ◽  
City Model ◽  
Region Growth

Urban viaducts are important infrastructures for the transportation system of a city. In this paper, an original method is proposed to automatically extract urban viaducts and reconstruct topology of the viaduct network just with airborne LiDAR point cloud data. It will greatly simplify the effort-taking procedure of viaducts extraction and reconstruction. In our method, the point cloud first is filtered to divide all the points into ground points and none-ground points. Region growth algorithm is adopted to find the viaduct points from the none-ground points by the features generated from its general prescriptive designation rules. Then, the viaduct points are projected into 2D images to extract the centerline of every viaduct and generate cubic functions to represent passages of viaducts by least square fitting, with which the topology of the viaduct network can be rebuilt by combining the height information. Finally, a topological graph of the viaducts network is produced. The full-automatic method can potentially benefit the application of urban navigation and city model reconstruction.

scholarly journals AN AUTOMATIC EXTRACTION METHOD FOR THE PARAMETERS OF MULTI-LOD BIM MODELS FOR TYPICAL COMPONENTS OF WOODEN ARCHITECTURAL HERITAGE

2019 ◽  
Vol XLII-2/W15 ◽  
pp. 679-685
Author(s):  
H. Liu ◽  
M. Hou ◽  
A. Li ◽  
L. Xie
Keyword(s):  
Song Dynasty ◽  
Extraction Method ◽  
Point Cloud ◽  
Point Clouds ◽  
Model Parameters ◽  
Automatic Extraction ◽  
High Fidelity ◽  
Point Cloud Data ◽  
Architectural Heritage ◽  
Cloud Data

<p><strong>Abstract.</strong> A demand-oriented Building Information Model (BIM) model built using high-fidelity point cloud data can better protect architectural heritage. The multi-level detail (mutli-LoD) parametric model emphasizes the different protection requirements of typical components and the automatic extraction of corresponding parameters of high-fidelity point clouds, which are two related key issues. Taking the typical Chinese wooden architectural heritage as an example, according to different requirements, the multi-LoD principle of typical components is proposed. On this basis, the automatic extraction method of the above parameters is developed, and the key parameters of the method are recommended. In order to solve the above problems, taking the three typical Dou-Gong used in Liao Dynasty and Song Dynasty, including Zhutou Puzuo, Bujian Puzuo and Zhuanjiao Puzuo, as an example, briefly introduced the standardization characteristics of the typical components of the "Yingzao Fashi". Subsequently, the corresponding multiple LoD principles are recommended according to different requirements. Based on this and high-fidelity point cloud data, an automatic extraction method for multi-LoD BIM model parameters for typical components of wooden architectural heritage is proposed.</p>

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.

ESTIMATION OF VEGETATION DENSITY OF TREES USING ALB POINT CLOUD DATA

2018 ◽  
Vol 74 (4) ◽  
pp. I_547-I_552
Author(s):  
Keisuke YOSHIDA ◽  
Shiro MAENO ◽  
Syuhei OGAWA ◽  
Sadayuki ISEKI ◽  
Ryosuke AKOH
Keyword(s):  
Point Cloud ◽  
Point Cloud Data ◽  
Vegetation Density ◽  
Cloud Data
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