scholarly journals Research for Generating Three-Dimensional Model of Bridge with Point Cloud Data

2015 ◽  
Vol 27 (5) ◽  
pp. 796-812 ◽  
Author(s):  
Yoshinori TSUKADA ◽  
Shigenori TANAKA ◽  
Satoshi KUBOTA ◽  
Kenji NAKAMURA ◽  
Hideki OKANAKA
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Point Cloud Data ◽  
Dimensional Model ◽  
Three Dimensional Model ◽  
Cloud Data

scholarly journals PHOTOGRAMMETRIC MODELING OF SUBTERRANEAN FEATURES THROUGH THREE-DIMENSIONAL SOFTWARE ANALYSIS

2020 ◽  
Vol XLIV-M-2-2020 ◽  
pp. 95-98
Author(s):  
J. Sepulveda ◽  
J. Capps ◽  
K. Johnson ◽  
C. Parada ◽  
A. Garcia ◽  
...  
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Point Cloud Data ◽  
Dimensional Model ◽  
Three Dimensional Model ◽  
Cloud Data ◽  
Software Analysis ◽  
Cave System ◽  
Set Up ◽  
The Impact

Abstract. LiDAR is a popular and accurate method for mapping that can be utilized for three-dimensional model analysis. However, the equipment set-up and usage can become tedious, and ultimately impractical when applied to locations that are remote and confined in nature. In this investigation, three-dimensional analysis was conducted within a cave system. With this, limitations of LiDAR technology in these conditions become prominent; mapping non-planar surfaces can cause a potential decrease of the quality of the point cloud data. In all, a LiDAR application would be an inefficient use of methodology to conduct this investigation. This prompted a need to set-up and conduct a photogrammetric based evaluation. With this, smartphone camera technology was used in conjunction with free-to-use software and three-dimensional modeling applications. Through the use of photogrammetric concepts and structure from motion software, a three-dimensional model of the cave can be generated. Long term, this model can also be utilized to document the impact and health of the cave system. For the methodology, the on-sight portion of the investigation relied heavily on smartphone camera technology. The procedure draws parallels to drone paths; specifically, two flight-plans were developed to evaluate different perspectives within a 15 by 15 meter space in the cave. Within each flight path, the use of photo overlapping techniques established a denser and more fluid point cloud model. Once the data was processed, two different three-dimensional models of the cave were created. From those models, the point cloud data was extracted in order to merge the two separate models. Afterwards, the models underwent several format conversions in order to import it into the Unity game engine. The final result is an accurate three-dimensional model of the cave that is viewable and playable in a simple video game platform.

scholarly journals A Novel Method for Density Analysis of Repaired Point Cloud with Holes Based on Image Data

2021 ◽  
Vol 13 (17) ◽  
pp. 3417
Author(s):  
Yibo He ◽  
Zhenqi Hu ◽  
Kan Wu ◽  
Rui Wang
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Image Data ◽  
Image Point ◽  
Point Cloud Data ◽  
Three Dimensional Model ◽  
Cloud Data ◽  
Density Analysis ◽  
Novel Method ◽  
Density Image

Repairing point cloud holes has become an important problem in the research of 3D laser point cloud data, which ensures the integrity and improves the precision of point cloud data. However, for the point cloud data with non-characteristic holes, the boundary data of point cloud holes cannot be used for repairing. Therefore, this paper introduces photogrammetry technology and analyzes the density of the image point cloud data with the highest precision. The 3D laser point cloud data are first formed into hole data with sharp features. The image data are calculated into six density image point cloud data. Next, the barycenterization Bursa model is used to fine-register the two types of data and to delete the overlapping regions. Then, the cross-section is used to evaluate the precision of the combined point cloud data to get the optimal density. A three-dimensional model is constructed for this data and the original point cloud data, respectively and the surface area method and the deviation method are used to compare them. The experimental results show that the ratio of the areas is less than 0.5%, and the maximum standard deviation is 0.0036 m and the minimum is 0.0015 m.

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

METHOD OF REGISTRATION FOR 3D FACE POINT CLOUD DATA

2015 ◽  
Vol 75 (2) ◽  
Author(s):  
Mohd Kufaisal Mohd Sidik ◽  
Mohd Shahrizal Sunar ◽  
Muhamad Najib Zamri
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Human Head ◽  
Point Of View ◽  
Front Face ◽  
Point Cloud Data ◽  
3D Face ◽  
Cloud Data ◽  
Data Registration ◽  
The Face

This paper analyzes the techniques that can be used to perform point cloud data registration for a human face. We found that there is a limitation in full scale viewing on the input data taken from 3D camera which is only represented the front face of a man as the point of view of a camera. This has caused a hole on the surface that is not filled with the point cloud data. This research is done by mapping the retrieved point cloud to the surface of the face template of the human head. By using Coherent Point Drift (CPD) algorithm which is one of the non-rigid registration techniques, the analysis has been done and it shows that the mapping of points for a three-dimensional (3D) face is not done properly where there are some surfaces work well and certain points spread into the wrong area. Consequently, it has resulted in registration failure occurrences due to the concentration of the points which is focusing on the face only.

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