Automatic extraction method study of road marking lines based on projection of point clouds

2014 ◽  
Author(s):  
Yuan Yao ◽  
Qingwu Hu
Keyword(s):  
Extraction Method ◽  
Point Clouds ◽  
Automatic Extraction ◽  
Road Marking

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>

scholarly journals Extraction of Guardrails from MMS Data Using Convolutional Neural Network

2021 ◽  
Vol 15 (3) ◽  
pp. 258-267
Author(s):  
Hiroki Matsumoto ◽  
◽  
Yuma Mori ◽  
Hiroshi Masuda
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Asset Management ◽  
Extraction Method ◽  
Point Clouds ◽  
Mobile Mapping ◽  
Method Point ◽  
Mobile Mapping Systems ◽  
Capture Point ◽  
Mapping Systems

Mobile mapping systems can capture point clouds and digital images of roadside objects. Such data are useful for maintenance, asset management, and 3D map creation. In this paper, we discuss methods for extracting guardrails that separate roadways and walkways. Since there are various shape patterns for guardrails in Japan, flexible methods are required for extracting them. We propose a new extraction method based on point processing and a convolutional neural network (CNN). In our method, point clouds and images are segmented into small fragments, and their features are extracted using CNNs for images and point clouds. Then, features from images and point clouds are combined and investigated using whether they are guardrails or not. Based on our experiments, our method could extract guardrails from point clouds with a high success rate.

Sign in / Sign up

Export Citation Format

Share Document