Industrial Design Applications of Surface Reconstruction Algorithm Based on Three Dimensional Point Cloud Data

2017 ◽  
Author(s):  
Yang Haibo
Keyword(s):  
Surface Reconstruction ◽  
Point Cloud ◽  
Industrial Design ◽  
Three Dimensional ◽  
Reconstruction Algorithm ◽  
Point Cloud Data ◽  
Cloud Data

Improved Three-Dimensional Reconstruction Algorithm for Point Cloud Data

2020 ◽  
Vol 57 (2) ◽  
pp. 021102
Author(s):  
庞正雅 Pang Zhengya ◽  
周志峰 Zhou Zhifeng ◽  
王立端 Wang Liduan ◽  
叶珏磊 Ye Juelei
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Reconstruction Algorithm ◽  
Point Cloud Data ◽  
Three Dimensional Reconstruction ◽  
Cloud Data ◽  
Dimensional Reconstruction

scholarly journals A Lightweight Surface Reconstruction Method for Online 3D Scanning Point Cloud Data Oriented toward 3D Printing

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Buyun Sheng ◽  
Feiyu Zhao ◽  
Xiyan Yin ◽  
Chenglei Zhang ◽  
Hui Wang ◽  
...  
Keyword(s):  
3D Printing ◽  
Surface Reconstruction ◽  
Point Cloud ◽  
Reconstruction Algorithm ◽  
3D Scanning ◽  
Reconstruction Method ◽  
Point Cloud Data ◽  
Registration Method ◽  
Cloud Data ◽  
Web Environment

The existing surface reconstruction algorithms currently reconstruct large amounts of mesh data. Consequently, many of these algorithms cannot meet the efficiency requirements of real-time data transmission in a web environment. This paper proposes a lightweight surface reconstruction method for online 3D scanned point cloud data oriented toward 3D printing. The proposed online lightweight surface reconstruction algorithm is composed of a point cloud update algorithm (PCU), a rapid iterative closest point algorithm (RICP), and an improved Poisson surface reconstruction algorithm (IPSR). The generated lightweight point cloud data are pretreated using an updating and rapid registration method. The Poisson surface reconstruction is also accomplished by a pretreatment to recompute the point cloud normal vectors; this approach is based on a least squares method, and the postprocessing of the PDE patch generation was based on biharmonic-like fourth-order PDEs, which effectively reduces the amount of reconstructed mesh data and improves the efficiency of the algorithm. This method was verified using an online personalized customization system that was developed with WebGL and oriented toward 3D printing. The experimental results indicate that this method can generate a lightweight 3D scanning mesh rapidly and efficiently in a web environment.

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.

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