Study on the 3D Reconstruction Technology Based on Point Cloud

2013 ◽  
Vol 427-429 ◽  
pp. 1731-1734 ◽  
Author(s):  
Xiao Xu Leng ◽  
Jun Xiao ◽  
Deng Yu Lee ◽  
Jiao Rao Su
Keyword(s):  
3D Reconstruction ◽  
Point Cloud ◽  
Rapid Development ◽  
Three Dimensional ◽  
Development Trend ◽  
Practical Significance ◽  
3D Point Cloud ◽  
Point Cloud Data ◽  
Cloud Data ◽  
The Development Trend

With the rapid development of various three dimensional scanning devices, the 3D point cloud data of many objects in the real world can be easily obtained. Therefore, research on 3D reconstruction technology based on point cloud has important practical significance. In this paper, first the research background of point cloud reconstruction is analyzed; and then the typical methods for reconstruction are presented and discussed; finally the performance of these methods are analyzed and compared qualitatively, and the development trend in this field is prospected.

Automated Surface Inspection Using 3D Point Cloud Data in Manufacturing: A Case Study

2018 ◽  
Author(s):  
Romina Dastoorian ◽  
Ahmad E. Elhabashy ◽  
Wenmeng Tian ◽  
Lee J. Wells ◽  
Jaime A. Camelio
Keyword(s):  
Point Cloud ◽  
Surface Defect ◽  
Fault Location ◽  
Three Dimensional ◽  
Practical Case ◽  
3D Point Cloud ◽  
Point Cloud Data ◽  
Defect Inspection ◽  
Cloud Data

With the latest advancements in three-dimensional (3D) measurement technologies, obtaining 3D point cloud data for inspection purposes in manufacturing is becoming more common. While 3D point cloud data allows for better inspection capabilities, their analysis is typically challenging. Especially with unstructured 3D point cloud data, containing coordinates at random locations, the challenges increase with higher levels of noise and larger volumes of data. Hence, the objective of this paper is to extend the previously developed Adaptive Generalized Likelihood Ratio (AGLR) approach to handle unstructured 3D point cloud data used for automated surface defect inspection in manufacturing. More specifically, the AGLR approach was implemented in a practical case study to inspect twenty-seven samples, each with a unique fault. These faults were designed to cover an array of possible faults having three different sizes, three different magnitudes, and located in three different locations. The results show that the AGLR approach can indeed differentiate between non-faulty and a varying range of faulty surfaces while being able to pinpoint the fault location. This work also serves as a validation for the previously developed AGLR approach in a practical scenario.

scholarly journals THE RESEARCH ON ACCURACY EVALUATION METHOD OF THE TOMOGRAPHIC SAR THREE-DIMENSIONAL RECONSTRUCTION OF URBAN BUILDING BASED ON TERRESTRIAL LIDAR POINT CLOUD

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 617-621
Author(s):  
L. Li ◽  
L. Pang ◽  
X. D. Zhang ◽  
H. Liu
Keyword(s):  
Point Cloud ◽  
Evaluation Method ◽  
Three Dimensional ◽  
Accuracy Evaluation ◽  
3D Point Cloud ◽  
Point Cloud Data ◽  
Terrestrial Lidar ◽  
Cloud Data ◽  
Dimensional Precision ◽  
Urban Buildings

Muti-baseLine SAR tomography can be used on 3D reconstruction of urban building based on SAR images acquired. In the near future, it is expected to become an important technical tool for urban multi-dimensional precision monitoring. For the moment，There is no effective method to verify the accuracy of tomographic SAR 3D point cloud of urban buildings. In this paper, a new method based on terrestrial Lidar 3D point cloud data to verify the accuracy of the tomographic SAR 3D point cloud data is proposed, 3D point cloud of two can be segmented into different facadeds. Then facet boundary extraction is carried out one by one, to evaluate the accuracy of tomographic SAR 3D point cloud of urban buildings. The experience select data of Pangu Plaza to analyze and compare, the result of experience show that the proposed method that evaluating the accuracy of tomographic SAR 3D point clou of urban building based on lidar 3D point cloud is validity and applicability

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.

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.

Registration Algorithm for Three-Dimensional Point Cloud Data

2015 ◽  
Vol 741 ◽  
pp. 237-240
Author(s):  
Li Lun Huang ◽  
Wen Guo Li ◽  
Qi Le Yang ◽  
Ying Chun Chen
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Singular Value ◽  
3D Point Cloud ◽  
Point Cloud Data ◽  
Icp Algorithm ◽  
Cloud Data ◽  
Registration Algorithm ◽  
Mathematical Algorithms ◽  
Value Decomposition

The principle of registration of the 3D point cloud data and the current algorithms are compared, and ICP algorithm is chosen since its fast convergence speed, high precision, and simple objective function. On the basis of ICP algorithm, singular value decomposition and four-array method are analysed by programming program, and all the mathematical algorithms is transformed into programming language by Matlab software.

scholarly journals Key Technologies of Seam Fusion for Multi-view Image Texture Mapping Based on 3D Point Cloud Data

2021 ◽  
Vol 2066 (1) ◽  
pp. 012042
Author(s):  
Xiaoxue Yang
Keyword(s):  
Point Cloud ◽  
Rapid Development ◽  
Experimental Studies ◽  
Texture Mapping ◽  
Image Texture ◽  
Measurement Technology ◽  
3D Point Cloud ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Joint Coding

Abstract With the rapid development of computer technology and measurement technology, three-dimensional point cloud data, as an important form of data in computer graphics, is used by light reactions in reverse engineering, surveying, robotics, virtual reality, stereo 3D imaging, Indoor scene reconstruction and many other fields. This paper aims to study the key technology of 3D point cloud data multi-view image texture mapping seam fusion, and propose a joint coding and compression scheme of multi-view image texture to replace the previous independent coding scheme of applying MVC standard compression to multi-view image texture. Experimental studies have shown that multi-view texture depth joint coding has different degrees of performance improvement compared with the other two current 3D MVD data coding schemes. Especially for Ballet and Dancer sequences with better depth video quality, the performance of JMVDC is very obvious. Compared with the KS_ IBP structure, the gain can reach as high as 1.34dB at the same bit rate.

Segmentation Algorithm of Three-Dimensional Point Cloud Data Based on Region Growing

2015 ◽  
Vol 741 ◽  
pp. 382-385 ◽  
Author(s):  
Li Lun Huang ◽  
Wen Guo Li ◽  
Qi Le Yang ◽  
Ying Chun Chen
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Main Idea ◽  
Region Growing ◽  
Segmentation Algorithm ◽  
3D Point Cloud ◽  
Point Cloud Data ◽  
Seed Point ◽  
Cloud Data ◽  
Set Of Points

Segmentation algorithm of 3D point cloud data based on region growing is proposed, the main idea is as follows: First, seed points in each region of object surface are searched, and then, starts from the seed point, the process of regional growing is done, which all the point cloud data belong to same surface are included until some discontinuous set of points appear. The algorithm is implemented under C, and the 3D point cloud data are showed by OPENGL software.

scholarly journals A LiDAR Point Cloud Data-Based Method for Evaluating Strain on a Curved Steel Plate Subjected to Lateral Pressure

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 721
Author(s):  
Hyeon Cheol Jo ◽  
Hong-Gyoo Sohn ◽  
Yun Mook Lim
Keyword(s):  
Point Cloud ◽  
Safety Assessment ◽  
Laser Scanning ◽  
Evaluation Method ◽  
Three Dimensional ◽  
Steel Structures ◽  
3D Point Cloud ◽  
Point Cloud Data ◽  
Lidar System ◽  
Cloud Data

Structural health monitoring (SHM) and safety assessment are very important areas for evaluating the behavior of structures. Various wired and wireless sensors can measure the physical responses of structures, such as displacement or strain. One recently developed wireless technique is a light imaging detection and ranging (LiDAR) system that can remotely acquire three-dimensional (3D) high-precision coordinate information using 3D laser scanning. LiDAR systems have been previously used in geographic information systems (GIS) to collect information on geography and terrain. Recently, however, LiDAR is used in the SHM field to analyze structural behavior, as it can remotely detect the surface and deformation shape of structures without the need for attached sensors. This study demonstrates a strain evaluation method using a LiDAR system in order to analyze the behavior of steel structures. To evaluate the strains of structures from the initial and deformed shape, a combination of distributed 3D point cloud data and finite element methods (FEM) was used. The distributed 3D point cloud data were reconstructed into a 3D mesh model, and strains were calculated using the FEM. By using the proposed method, the strain could be calculated at any point on a structure for SHM and safety assessment during construction.

scholarly journals Estimation of shoulder width and neck girth based on 3D point cloud data

2021 ◽  
pp. 47-47
Author(s):  
Xin Lu ◽  
Panpan Guo ◽  
Guolian Liu
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Young Female ◽  
3D Measurement ◽  
3D Point Cloud ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Point Cloud Simplification

Three dimensional point cloud map in the anthropometry has attracted intensive attention due to the availability of fast and accurate laser scan devices. Inevitably, there is a data deviation between 3D measurement and manual tests. To address this problem, shoulder width and neck girth are accurately determined from 3D point cloud, the two-scale fractal is used for 3D point cloud simplification, and young female samples are used in our experiment to show the accuracy.

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