An automatic segmentation method for building facades from vehicle-borne LiDAR point cloud data based on fundamental geographical data

2016 ◽  
Author(s):  
Yongqiang Li ◽  
Jie Mao ◽  
Lailiang Cai ◽  
Xitong Zhang ◽  
Lixue Li
Keyword(s):  
Point Cloud ◽  
Automatic Segmentation ◽  
Point Cloud Data ◽  
Segmentation Method ◽  
Geographical Data ◽  
Cloud Data ◽  
Building Facades

scholarly journals The Segmentation Method of Target Point Cloud for Polarization-Modulated 3D Imaging

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 179 ◽  
Author(s):  
Shengjie Wang ◽  
Bo Liu ◽  
Zhen Chen ◽  
Heping Li ◽  
Shuo Jiang
Keyword(s):  
Time Resolution ◽  
Point Cloud ◽  
3D Imaging ◽  
Test Method ◽  
Target Point ◽  
Point Cloud Data ◽  
Segmentation Method ◽  
Electron Multiplier ◽  
Cloud Data ◽  
Gray Image

To implement target point cloud segmentation for a polarization-modulated 3D imaging system in practical projects, an efficient segmentation concept of multi-dimensional information fusion is designed. As the electron multiplier charge coupled device (EMCCD) camera can only acquire the gray image, but has no ability for time resolution owing to the time integration mechanism, large diameter electro-optic modulators (EOM) are used to provide time resolution for the EMCCD cameras to obtain the polarization-modulated images, from which a 3D image can also be simultaneously reconstructed. According to the characteristics of the EMCCD camera’s plane array imaging, the point-to-point mapping relationship between the gray image pixels and point cloud data coordinates is established. The target’s pixel coordinate position obtained by image segmentation is mapped to 3D point cloud data to get the segmented target point cloud data. On the basis of the specific environment characteristics of the experiment, the maximum entropy test method is applied to the target segmentation of the gray image, and the image morphological erosion algorithm is used to improve the segmentation accuracy. This method solves the problem that conventional point clouds’ segmentation methods cannot effectively segment irregular objects or closely bound objects. Meanwhile, it has strong robustness and stability in the presence of noise, and reduces the computational complexity and data volume. The experimental results show that this method is better for the segmentation of the target in the actual environment, and can avoid the over-segmentation and under-segmentation to some extent. This paper illustrates the potential and feasibility of the segmentation method based on this system in real-time data processing.

scholarly journals An Automatic Density Clustering Segmentation Method for Laser Scanning Point Cloud Data of Buildings

2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Jianghong Zhao ◽  
Yan Dong ◽  
Siyu Ma ◽  
Huajun Liu ◽  
Shuangfeng Wei ◽  
...  
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Three Dimensional ◽  
Point Cloud Data ◽  
Segmentation Method ◽  
Cloud Data ◽  
Dbscan Algorithm ◽  
Density Clustering ◽  
Low Efficiency ◽  
Definition Of

Segmentation is an important step in point cloud data feature extraction and three-dimensional modelling. Currently, it is also a challenging problem in point cloud processing. There are some disadvantages of the DBSCAN method, such as requiring the manual definition of parameters and low efficiency when it is used for large amounts of calculation. This paper proposes the AQ-DBSCAN algorithm, which is a density clustering segmentation method combined with Gaussian mapping. The algorithm improves upon the DBSCAN algorithm by solving the problem of automatic estimation of the parameter neighborhood radius. The improved algorithm can carry out density clustering processing quickly by reducing the amount of computation required.

An Automatic Segmentation Algorithm for Dense Pipeline Point Cloud Data

2018 ◽  
Vol 45 (11) ◽  
pp. 1104004
Author(s):  
黄凯 Huang Kai ◽  
程效军 Cheng Xiaojun ◽  
贾东峰 Jia Dongfeng ◽  
胡旦华 Hu Danhua ◽  
胡敏捷 Hu Minjie
Keyword(s):  
Point Cloud ◽  
Automatic Segmentation ◽  
Segmentation Algorithm ◽  
Point Cloud Data ◽  
Cloud Data

scholarly journals SCAN PROFILES BASED METHOD FOR SEGMENTATION AND EXTRACTION OF PLANAR OBJECTS IN MOBILE LASER SCANNING POINT CLOUDS

2016 ◽  
Vol XLI-B3 ◽  
pp. 351-358 ◽  
Author(s):  
Hoang Long Nguyen ◽  
David Belton ◽  
Petra Helmholz
Keyword(s):  
Spatial Data ◽  
Point Cloud ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Point Clouds ◽  
Point Cloud Data ◽  
Segmentation Method ◽  
Cloud Data ◽  
Processing Steps ◽  
Carrier Vehicle

The demand for accurate spatial data has been increasing rapidly in recent years. Mobile laser scanning (MLS) systems have become a mainstream technology for measuring 3D spatial data. In a MLS point cloud, the point clouds densities of captured point clouds of interest features can vary: they can be sparse and heterogeneous or they can be dense. This is caused by several factors such as the speed of the carrier vehicle and the specifications of the laser scanner(s). The MLS point cloud data needs to be processed to get meaningful information e.g. segmentation can be used to find meaningful features (planes, corners etc.) that can be used as the inputs for many processing steps (e.g. registration, modelling) that are more difficult when just using the point cloud. Planar features are dominating in manmade environments and they are widely used in point clouds registration and calibration processes. There are several approaches for segmentation and extraction of planar objects available, however the proposed methods do not focus on properly segment MLS point clouds automatically considering the different point densities. This research presents the extension of the segmentation method based on planarity of the features. This proposed method was verified using both simulated and real MLS point cloud datasets. The results show that planar objects in MLS point clouds can be properly segmented and extracted by the proposed segmentation method.

A new segmentation method for point cloud data

2002 ◽  
Vol 42 (2) ◽  
pp. 167-178 ◽  
Author(s):  
H. Woo ◽  
E. Kang ◽  
Semyung Wang ◽  
Kwan H. Lee
Keyword(s):  
Point Cloud ◽  
Point Cloud Data ◽  
Segmentation Method ◽  
Cloud Data

scholarly journals SCAN PROFILES BASED METHOD FOR SEGMENTATION AND EXTRACTION OF PLANAR OBJECTS IN MOBILE LASER SCANNING POINT CLOUDS

2016 ◽  
Vol XLI-B3 ◽  
pp. 351-358 ◽  
Author(s):  
Hoang Long Nguyen ◽  
David Belton ◽  
Petra Helmholz
Keyword(s):  
Spatial Data ◽  
Point Cloud ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Point Clouds ◽  
Point Cloud Data ◽  
Segmentation Method ◽  
Cloud Data ◽  
Processing Steps ◽  
Carrier Vehicle

The demand for accurate spatial data has been increasing rapidly in recent years. Mobile laser scanning (MLS) systems have become a mainstream technology for measuring 3D spatial data. In a MLS point cloud, the point clouds densities of captured point clouds of interest features can vary: they can be sparse and heterogeneous or they can be dense. This is caused by several factors such as the speed of the carrier vehicle and the specifications of the laser scanner(s). The MLS point cloud data needs to be processed to get meaningful information e.g. segmentation can be used to find meaningful features (planes, corners etc.) that can be used as the inputs for many processing steps (e.g. registration, modelling) that are more difficult when just using the point cloud. Planar features are dominating in manmade environments and they are widely used in point clouds registration and calibration processes. There are several approaches for segmentation and extraction of planar objects available, however the proposed methods do not focus on properly segment MLS point clouds automatically considering the different point densities. This research presents the extension of the segmentation method based on planarity of the features. This proposed method was verified using both simulated and real MLS point cloud datasets. The results show that planar objects in MLS point clouds can be properly segmented and extracted by the proposed segmentation method.

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