scholarly journals Reconstruction of Two-dimensional Image to Three-dimensional Point Cloud Data for Detection of Empty Rot Defects in Tree Growth

CONVERTER ◽  
2021 ◽  
pp. 459-470
Author(s):  
Shufeng Jiang, Keqi Wang
Keyword(s):  
Point Cloud ◽  
Color Space ◽  
Three Dimensional ◽  
Boundary Curve ◽  
Data Reconstruction ◽  
Two Dimensional ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Extraction Algorithm ◽  
Radar Wave

In the application of nondestructive detecting of trees, it is a technical problem to use radar waves to detect tree specimens with growth defects, how to segment defect areas after obtaining two-dimensional images, and reverse simulate the detection results with three-dimensional point cloud data. Therefore, the method of extracting boundary information according to color features is studied to extract the boundary curve of empty rot area, and the selection of higher precision extraction algorithm is determined by comparing the boundary extraction results of HSV color space and RGB color space in laboratory According to the extracted void boundary curve, the reverse modeling is carried out, and the mapping from 2D inspection gray image to 3D space is realized, The point cloud data reconstruction needed for 3D modeling of multi-curved surfaces is obtained in reverse. The boundary curve extraction algorithm in this study is used to process the images of nondestructive testing of trees. Through comparative experiments and error analysis, the accurate modeling conclusion from inversion of 2D images to 3D point cloud data reconstruction by radar wave detection is verified, and the Core issue problem of point cloud reconstruction in the ill-conditioned area of tree growth and decay detected by radar wave is solved.

The Method and Implementation of Three-Dimensional Laser Scanning Pipeline Scan Data Outliers Excluded

2013 ◽  
Vol 864-867 ◽  
pp. 2760-2763
Author(s):  
Zhi Liang Li ◽  
He Sheng Zhang ◽  
Qi Wu
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Three Dimensional ◽  
Two Dimensional ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Handling Characteristics ◽  
Gross Error ◽  
Error Elimination ◽  
Scan Data

This article is based on three-dimensional laser scanning technology for the modeling of a chemical plant piping, scanned point cloud data with a lot of blunders, comprehensive analysis of the point cloud handling characteristics and stage of maturity of two-dimensional graphics. As a result, a concept of transforming the point cloud data with three dimensional to two-dimensional is formed. Then, according to point and circle positional relationship in the same plane, derived an Algorithm about Gross Error Elimination, finally, programming and achieve it.

scholarly journals Multi-Dimensional Underwater Point Cloud Detection Based on Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 884
Author(s):  
Chia-Ming Tsai ◽  
Yi-Horng Lai ◽  
Yung-Da Sun ◽  
Yu-Jen Chung ◽  
Jau-Woei Perng
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Three Dimensional ◽  
Point Clouds ◽  
Training Data ◽  
Network Architectures ◽  
Point Cloud Data ◽  
Data Types ◽  
Raw Data ◽  
Cloud Data

Numerous sensors can obtain images or point cloud data on land, however, the rapid attenuation of electromagnetic signals and the lack of light in water have been observed to restrict sensing functions. This study expands the utilization of two- and three-dimensional detection technologies in underwater applications to detect abandoned tires. A three-dimensional acoustic sensor, the BV5000, is used in this study to collect underwater point cloud data. Some pre-processing steps are proposed to remove noise and the seabed from raw data. Point clouds are then processed to obtain two data types: a 2D image and a 3D point cloud. Deep learning methods with different dimensions are used to train the models. In the two-dimensional method, the point cloud is transferred into a bird’s eye view image. The Faster R-CNN and YOLOv3 network architectures are used to detect tires. Meanwhile, in the three-dimensional method, the point cloud associated with a tire is cut out from the raw data and is used as training data. The PointNet and PointConv network architectures are then used for tire classification. The results show that both approaches provide good accuracy.

Research on Generation Rules of Chest Width Point Curve Based on 3D Female Mannequin

2013 ◽  
Vol 796 ◽  
pp. 513-518
Author(s):  
Rong Jin ◽  
Bing Fei Gu ◽  
Guo Lian Liu
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Upper Body ◽  
Secondary Development ◽  
Linear Regression Method ◽  
Point Cloud Data ◽  
Body Type ◽  
Cloud Data ◽  
Engineering Software ◽  
The Individual

In this paper 110 female undergraduates in Soochow University are measured by using 3D non-contact measurement system and manual measurement. 3D point cloud data of human body is taken as research objects by using anti-engineering software, and secondary development of point cloud data is done on the basis of optimizing point cloud data. In accordance with the definition of the human chest width points and other feature points, and in the operability of the three-dimensional point cloud data, the width, thickness, and length dimensions of the curve through the chest width point are measured. Classification of body type is done by choosing the ratio values as classification index which is the ratio between thickness and width of the curve. The generation rules of the chest curve are determined for each type by using linear regression method. Human arm model could be established by the computer automatically. Thereby the individual model of the female upper body mannequin modeling can be improved effectively.

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 Scattered Point Cloud Data Reconstruction Algorithm Based on Local Convexity

2020 ◽  
Author(s):  
Sorush Niknamian
Keyword(s):  
Point Cloud ◽  
Reconstruction Algorithm ◽  
Large Error ◽  
Small Error ◽  
Data Reconstruction ◽  
Point Cloud Data ◽  
Local Convexity ◽  
Cloud Data ◽  
Time Consumption ◽  
Local Connection

Point cloud data reconstruction is the basis of point cloud data processing. The reconstruction effect has a great impact on application. For the problems of low precision, large error, and high time consumption of the current scattered point cloud data reconstruction algorithm, a new algorithm of scattered point cloud data reconstruction based on local convexity is proposed in this paper. Firstly, according to surface variation based on local outlier factor (SVLOF), the noise points of point cloud data are divided into near outlier and far outlier, and filtered for point cloud data preprocessing. Based on this, the algorithm based on local convexity is improved. The method of constructing local connection point set is used to replace triangulation to analyze the relationship of neighbor points. The connection part identification method is used for data reconstruction. Experimental results show that, the proposed method can reconstruct the scattered point cloud data accurately, with high precision, small error and low time consumption.

scholarly journals VISUALIZATION OF THE CONSTRUCTION OF ANCIENT ROMAN BUILDINGS IN OSTIA USING POINT CLOUD DATA

2017 ◽  
Vol XLII-2/W3 ◽  
pp. 345-352
Author(s):  
Y. Hori ◽  
T. Ogawa
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Feature Matching ◽  
Three Dimensional ◽  
Point Clouds ◽  
Final Report ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Coverage Accuracy ◽  
New Research

The implementation of laser scanning in the field of archaeology provides us with an entirely new dimension in research and surveying. It allows us to digitally recreate individual objects, or entire cities, using millions of three-dimensional points grouped together in what is referred to as "point clouds". In addition, the visualization of the point cloud data, which can be used in the final report by archaeologists and architects, should usually be produced as a JPG or TIFF file. Not only the visualization of point cloud data, but also re-examination of older data and new survey of the construction of Roman building applying remote-sensing technology for precise and detailed measurements afford new information that may lead to revising drawings of ancient buildings which had been adduced as evidence without any consideration of a degree of accuracy, and finally can provide new research of ancient buildings. We used laser scanners at fields because of its speed, comprehensive coverage, accuracy and flexibility of data manipulation. Therefore, we “skipped” many of post-processing and focused on the images created from the meta-data simply aligned using a tool which extended automatic feature-matching algorithm and a popular renderer that can provide graphic results.

Processing and feature extraction for three-dimensional bullet point cloud data

2016 ◽  
Vol 31 (9) ◽  
pp. 889-896
Author(s):  
马鑫 MA Xin ◽  
魏仲慧 WEI Zhong-hui ◽  
何昕 HE Xin ◽  
于国栋 YU Guo-dong
Keyword(s):  
Feature Extraction ◽  
Point Cloud ◽  
Three Dimensional ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Bullet Point
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