3D point cloud data processing with machine learning for construction and infrastructure applications: A comprehensive review

Data processing is a key to reverse engineering, the results of which will directly affect the quality of the model reconstruction. Eliminate noise points are the first step in data processing, The method of using Coons surface to determine the noise in the data point is proposed. To reduce the amount of calculation and improve the surface generation efficiency, data point is reduced. According to the surrounding point coordinate information, the defect coordinates are interpolated. Data smoothing can improve the surface generation quality, data block can simplify the creation of the surface. Auto parts point cloud data is processed, and achieve the desired effect.

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Point Cloud Data Processing Method of Cavity 3D Laser Scanner

Acta Optica Sinica ◽

10.3788/aos201333.0812003 ◽

2013 ◽

Vol 33 (8) ◽

pp. 0812003 ◽

Cited By ~ 1

Author(s):

陈凯 Chen Kai ◽

张达 Zhang Da ◽

张元生 Zhang Yuansheng

Keyword(s):

Data Processing ◽

Point Cloud ◽

Laser Scanner ◽

Processing Method ◽

Point Cloud Data ◽

Cloud Data ◽

Data Processing Method ◽

Cloud Data Processing

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Triangulation-Based Point Cloud Data Processing Technology Research and Application

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.610.729 ◽

2014 ◽

Vol 610 ◽

pp. 729-733

Author(s):

Ke He Wu ◽

Wen Chao Cui ◽

Bo Hao Cheng ◽

Qian Yuan Zhang

Keyword(s):

Data Processing ◽

Data Storage ◽

Point Cloud ◽

Extraction Process ◽

Programming Algorithm ◽

Point Cloud Data ◽

Boundary Extraction ◽

Processing Technologies ◽

Cloud Data ◽

Cloud Data Processing

With the "Digital Earth" concept being put forward, people are starting to focus on geospatial information technology. Traditional manual building modeling process is gradually eliminated by history due to cumbersome and inefficient work. With massive data storage and processing technologies emerging and improving, people begin to explore building point cloud data measured by laser radar technology and to use point cloud data processing software for further building boundary extraction. In the model boundary extraction process, the use of prototype with the model fit is a good, clear and easy programming algorithm and triangulation algorithm.

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Efficient point cloud data processing in shipbuilding: Reformative component extraction method and registration method

Journal of Computational Design and Engineering ◽

10.7315/jcde.2014.020 ◽

2014 ◽

Vol 1 (3) ◽

pp. 202-212 ◽

Cited By ~ 4

Author(s):

Jingyu Sun ◽

Kazuo Hiekata ◽

Hiroyuki Yamato ◽

Norito Nakagaki ◽

Akiyoshi Sugawara

Keyword(s):

Data Processing ◽

Point Cloud ◽

Accuracy Evaluation ◽

Point Cloud Data ◽

Continuous Part ◽

Registration Method ◽

Shipbuilding Industry ◽

Seed Point ◽

Cloud Data ◽

Cloud Data Processing

Abstract To survive in the current shipbuilding industry, it is of vital importance for shipyards to have the ship components' accuracy evaluated efficiently during most of the manufacturing steps. Evaluating components' accuracy by comparing each component's point cloud data scanned by laser scanners and the ship's design data formatted in CAD cannot be processed efficiently when (1) extract components from point cloud data include irregular obstacles endogenously, or when (2) registration of the two data sets have no clear direction setting. This paper presents reformative point cloud data processing methods to solve these problems. K-d tree construction of the point cloud data fastens a neighbor searching of each point. Region growing method performed on the neighbor points of the seed point extracts the continuous part of the component, while curved surface fitting and B-spline curved line fitting at the edge of the continuous part recognize the neighbor domains of the same component divided by obstacles' shadows. The ICP (Iterative Closest Point) algorithm conducts a registration of the two sets of data after the proper registration's direction is decided by principal component analysis. By experiments conducted at the shipyard, 200 curved shell plates are extracted from the scanned point cloud data, and registrations are conducted between them and the designed CAD data using the proposed methods for an accuracy evaluation. Results show that the methods proposed in this paper support the accuracy evaluation targeted point cloud data processing efficiently in practice.

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LiDAR Point Cloud Data Processing in Autonomous Vehicles

10.1109/icecct52121.2021.9616784 ◽

2021 ◽

Author(s):

Vani Suthamathi Saravanarajan ◽

Rung-Ching Chen ◽

Long-Sheng Chen

Keyword(s):

Data Processing ◽

Autonomous Vehicles ◽

Point Cloud ◽

Point Cloud Data ◽

Cloud Data ◽

Cloud Data Processing

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An Improved DBSCAN Method for LiDAR Data Segmentation with Automatic Eps Estimation

Sensors ◽

10.3390/s19010172 ◽

2019 ◽

Vol 19 (1) ◽

pp. 172 ◽

Cited By ~ 8

Author(s):

Chunxiao Wang ◽

Min Ji ◽

Jian Wang ◽

Wei Wen ◽

Ting Li ◽

...

Keyword(s):

Data Processing ◽

Point Cloud ◽

Estimation Method ◽

Point Clouds ◽

Maximum Distance ◽

Point Cloud Data ◽

Cloud Data ◽

Data Segmentation ◽

Different Types ◽

Cloud Data Processing

Point cloud data segmentation, filtering, classification, and feature extraction are the main focus of point cloud data processing. DBSCAN (density-based spatial clustering of applications with noise) is capable of detecting arbitrary shapes of clusters in spaces of any dimension, and this method is very suitable for LiDAR (Light Detection and Ranging) data segmentation. The DBSCAN method needs at least two parameters: The minimum number of points minPts, and the searching radius ε. However, the parameter ε is often harder to determine, which hinders the application of the DBSCAN method in point cloud segmentation. Therefore, a segmentation algorithm based on DBSCAN is proposed with a novel automatic parameter ε estimation method—Estimation Method based on the average of k nearest neighbors’ maximum distance—with which parameter ε can be calculated on the intrinsic properties of the point cloud data. The method is based on the fitting curve of k and the mean maximum distance. The method was evaluated on different types of point cloud data: Airborne, and mobile point cloud data with and without color information. The results show that the accuracy values using ε estimated by the proposed method are 75%, 74%, and 71%, which are higher than those using parameters that are smaller or greater than the estimated one. The results demonstrate that the proposed algorithm can segment different types of LiDAR point clouds with higher accuracy in a robust manner. The algorithm can be applied to airborne and mobile LiDAR point cloud data processing systems, which can reduce manual work and improve the automation of data processing.

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