3D Reconstruction of Rock Mass Based on Point Cloud Library

2014 ◽  
Vol 543-547 ◽  
pp. 2920-2923
Author(s):  
Jun Xiao ◽  
Xiao Xu Leng ◽  
Deng Yu Li
Keyword(s):  
Parallel Computing ◽  
Rock Mass ◽  
3D Reconstruction ◽  
Point Cloud ◽  
Engineering Calculation ◽  
Point Cloud Data ◽  
Edge Extraction ◽  
Good Tool ◽  
Cloud Data ◽  
Cloud Data Processing

The Point Cloud Library (PCL) is a good tool for point cloud data processing. In this paper, a method of 3D reconstruction for rock mass based on PCL is introduced, where hardware choosing, parallel computing, PCL, and edge extraction are analyzed and used in order to realize a better reconstruction effect, including both precision and speed. The reconstruction results can be used in engineering calculation.

Research on BSP Algorithm of Construction of Large-Scale 3D Point Model

2014 ◽  
Vol 513-517 ◽  
pp. 461-465
Author(s):  
Song Liu ◽  
Xiao Yao Xie
Keyword(s):  
Parallel Computing ◽  
Data Processing ◽  
Point Cloud ◽  
Large Scale ◽  
Point Cloud Data ◽  
Point Model ◽  
Cloud Data ◽  
Data Processing Method ◽  
Low Efficiency ◽  
Cloud Data Processing

For the construction of large-scale surface features 3D point model, a large number of point cloud data processing calculations is needed. Previous model construction calculation was treated non-parallel manner successively and mostly with one by one point cloud. This data processing method is complex, low efficiency and requires vast computing resource. Accordance with the BSP parallel computing ideas, we design a point cloud data modeling algorithm based on BSP and build a Hama parallel computing cluster consisted of ordinary PCs. The results indicate that, large-scale 3D point model BSP construction algorithm can improve the efficiency of modeling calculations and reduce computing resources requirements for processing construction computing.

The Research of Point Cloud Data Processing Technology

2014 ◽  
Vol 628 ◽  
pp. 426-431
Author(s):  
Li Bo Zhou ◽  
Fu Lin Xu ◽  
Su Hua Liu
Keyword(s):  
Data Processing ◽  
Point Cloud ◽  
Quality Data ◽  
Surface Generation ◽  
Point Cloud Data ◽  
Data Smoothing ◽  
Cloud Data ◽  
Data Point ◽  
Cloud Data Processing

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.

Point Cloud Registrational Computing Based on Hadoop

2014 ◽  
Vol 644-650 ◽  
pp. 4624-4629
Author(s):  
Song Liu ◽  
Xiao Yao Xie
Keyword(s):  
Parallel Computing ◽  
Point Cloud ◽  
High Accuracy ◽  
Point Cloud Data ◽  
Point Cloud Registration ◽  
Computing Resource ◽  
Cloud Data ◽  
Registration Algorithm ◽  
Hadoop Cluster

For the problem of huge computation and requiring high computing resource in point cloud registration, according to the theory of parallel computing, the algorithm of point cloud registration base on MapReduce is designed. Through building a Hadoop cluster consisted by average PCs, four examples have been tested. The experiment results show that point cloud registration algorithm based on MapReduce can register point cloud data with high accuracy.

Three-Dimensional Reconstruction of Maize Plants and Extraction of Phenotypic Parameters Based On Laser Point Cloud Data

2021 ◽  
Author(s):  
Chengxin Ju ◽  
Yuanyuan Zhao ◽  
Fengfeng Wu ◽  
Rui Li ◽  
Tianle Yang ◽  
...  
Keyword(s):  
3D Reconstruction ◽  
Plant Height ◽  
Point Cloud ◽  
Agricultural Research ◽  
Three Dimensional ◽  
Laser Scanner ◽  
Point Cloud Data ◽  
Scanning Method ◽  
Cloud Data ◽  
Maize Plants

Abstract Background: Three-dimensional (3D) laser scanning technology could rapidly extract the surface geometric features of maize plants to achieve non-destructive monitoring of maize phenotypes. However, extracting the phenotypic parameters of maize plants based on laser point cloud data is challenging.Methods: In this paper, a rotational scanning method was used to collect the data of potted maize point cloud from different perspectives by using a laser scanner. Maize point cloud data were grid-reconstructed and aligned based on greedy projection triangulation algorithm and iterative closest point (ICP) algorithm, and the random sampling consistency algorithm was used to segment the stem and leaf point clouds of single maize plant to obtain the plant height and leaf parameters.Results: The results showed that the R2 between the predicted plant height and the measured plant height was above 0.95, and the R2 of the predicted leaf length, leaf width and leaf area were 0.938, 0878 and 0.956 respectively when compared with the measured values.Conclusions: The 3D reconstruction of maize plants using the laser scanner showed a good performance, and the phenotypic parameters obtained based on the reconstructed 3D model had high accuracy. The results were helpful to the practical application of plant 3D reconstruction and provided guidance for plant parameter acquisition and theoretical methods for intelligent agricultural research.

Point Cloud Data Processing Method of Cavity 3D Laser Scanner

2013 ◽  
Vol 33 (8) ◽  
pp. 0812003 ◽  
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

A Method of Point Cloud Stitching Based on the Mechanical Arm and Laser

2014 ◽  
Vol 988 ◽  
pp. 467-470
Author(s):  
Liang Liu ◽  
Shu Guang Dai
Keyword(s):  
3D Printing ◽  
3D Reconstruction ◽  
Coordinate System ◽  
Point Cloud ◽  
Three Dimensional ◽  
Good Effect ◽  
Point Cloud Data ◽  
Cloud Data

3D reconstruction as the basis of many applications,such as 3D printing, has become more and more importantfor many enterprises and researchersThe very important step in 3D reconstruction is the joining together of point cloud.This paper introduces the structures of a system to obtain three-dimensional point cloud data and a kind ofmethodsusing of the system to get point cloud data through the rotation and translation of the coordinate system, joining together the point cloud data.Experiment shows that this method has achieved good effect.

Triangulation-Based Point Cloud Data Processing Technology Research and Application

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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