<i>Three-dimensional cotton plant shoot architecture segmentation and phenotypic trait characterization using terrestrial LiDAR point cloud data </i>

2020 ◽  
Author(s):  
Shangpeng Sun ◽  
Changying Li ◽  
Andrew Paterson ◽  
Peng Chee
Keyword(s):  
Cotton Plant ◽  
Point Cloud ◽  
Three Dimensional ◽  
Phenotypic Trait ◽  
Point Cloud Data ◽  
Terrestrial Lidar ◽  
Cloud Data ◽  
Shoot Architecture

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

scholarly journals APPLICATION OF THE STEEP SLOPE RISK ASSESSMENT USING THREE DIMENSIONAL INFORMATION DATA

2019 ◽  
Vol XLII-3/W8 ◽  
pp. 381-386
Author(s):  
D. Y. Shin ◽  
J. S. Sim ◽  
K. S. Lee
Keyword(s):  
Risk Assessment ◽  
Inclination Angle ◽  
Point Cloud ◽  
Spatial Information ◽  
Three Dimensional ◽  
Steep Slope ◽  
Point Cloud Data ◽  
Registration Accuracy ◽  
Terrestrial Lidar ◽  
Cloud Data

<p><strong>Abstract.</strong> A collapse of slope is one of the natural disasters that often occur during the early spring and the rainy season. In order to prevent this kind of disaster, safety monitoring is carried out through risk assessment. This assessment consists of various parameters such as inclination angle and height of the slope, and inspectors evaluate the score using the compass, the laser range finder, and so on. This approach is, however, consumed a lot of the manpower and the time. This study, therefore, aims to evaluate the rapid and accurate steep slope risk by using a terrestrial LiDAR which takes 3 dimensional spatial information data. 3D spatial information data was acquired using the terrestrial LiDAR for steep slopes classified as very unstable slopes. Noise and vegetation of the acquired scan data were removed to generate point cloud data with a rock or mountain model without vegetation. The RMSE of the registration accuracy was 0.0156 m. From the point cloud data, the inclination angle, height, shape, valley, collapse and loss were evaluated. As a result, various risk assessment parameters can be checked at once. In addition, it is expected to be used as basic data for constructing steep slope DB, providing visualization data, and time series analysis in the future.</p>

scholarly journals Integration of Constructive Solid Geometry and Boundary Representation (CSG-BRep) for 3D Modeling of Underground Cable Wells from Point Clouds

2020 ◽  
Vol 12 (9) ◽  
pp. 1452
Author(s):  
Ming Huang ◽  
Xueyu Wu ◽  
Xianglei Liu ◽  
Tianhang Meng ◽  
Peiyuan Zhu
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Point Clouds ◽  
Adaptive Method ◽  
Boundary Representation ◽  
Point Cloud Data ◽  
Terrestrial Lidar ◽  
Cloud Data ◽  
Underground Cable ◽  
3D Information

The preference of three-dimensional representation of underground cable wells from two-dimensional symbols is a developing trend, and three-dimensional (3D) point cloud data is widely used due to its high precision. In this study, we utilize the characteristics of 3D terrestrial lidar point cloud data to build a CSG-BRep 3D model of underground cable wells, whose spatial topological relationship is fully considered. In order to simplify the modeling process, first, point cloud simplification is performed; then, the point cloud main axis is extracted by OBB bounding box, and lastly the point cloud orientation correction is realized by quaternion rotation. Furthermore, employing the adaptive method, the top point cloud is extracted, and it is projected for boundary extraction. Thereupon, utilizing the boundary information, we design the 3D cable well model. Finally, the cable well component model is generated by scanning the original point cloud. The experiments demonstrate that, along with the algorithm being fast, the proposed model is effective at displaying the 3D information of the actual cable wells and meets the current production demands.

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 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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