Research on Deformation Analysis of Time-Varying Point Cloud

2014 ◽  
Vol 556-562 ◽  
pp. 3450-3455 ◽  
Author(s):  
Song Liu ◽  
Lei Peng ◽  
Lin Lin Yuan
Keyword(s):  
Parallel Computing ◽  
Parallel Algorithm ◽  
Point Cloud ◽  
Large Scale ◽  
Personal Computers ◽  
Deformation Analysis ◽  
Time Varying ◽  
Analysis Method ◽  
Computing Technology ◽  
Computing Cluster

For large-scale object or scene which needs high requirements of deformation detection, a comprehensive deformation analysis method is proposed based on the time-varying point cloud to perform continuous detection, to comprehensively analyze the deformation and to research its characteristics and rules. In order to improve computing efficiency, a BSP parallel algorithm based on deformation analysis of time-varying point cloud is designed according to BSP parallel computing technology, and the deformational data are handled by a HAMA computing cluster which is composed of common personal computers. Several computing results from both simulations and real cases have proved the feasibility and effectiveness of analyzing method and BSP analyzing algorithm of deformation of time-varying point cloud.

scholarly journals DATUM FEATURE EXTRACTION AND DEFORMATION ANALYSIS METHOD BASED ON NORMAL VECTOR OF POINT CLOUD

2018 ◽  
Vol XLII-3 ◽  
pp. 1601-1606
Author(s):  
W. Sun ◽  
J. Wang ◽  
F. Jin ◽  
Z. Liang ◽  
Y. Yang
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Large Scale ◽  
Three Dimensional ◽  
Laser Scanner ◽  
Deformation Monitoring ◽  
Deformation Analysis ◽  
Normal Vector ◽  
Analysis Method ◽  
Data Set

In order to solve the problem lacking applicable analysis method in the application of three-dimensional laser scanning technology to the field of deformation monitoring, an efficient method extracting datum feature and analysing deformation based on normal vector of point cloud was proposed. Firstly, the kd-tree is used to establish the topological relation. Datum points are detected by tracking the normal vector of point cloud determined by the normal vector of local planar. Then, the cubic B-spline curve fitting is performed on the datum points. Finally, datum elevation and the inclination angle of the radial point are calculated according to the fitted curve and then the deformation information was analyzed. The proposed approach was verified on real large-scale tank data set captured with terrestrial laser scanner in a chemical plant. The results show that the method could obtain the entire information of the monitor object quickly and comprehensively, and reflect accurately the datum feature deformation.

Research on BSP Algorithm of Construction of Large-Scale 3D Point Model

2014 ◽  
Vol 513-517 ◽  
pp. 461-465
Author(s):  
Song Liu ◽  
Xiao Yao Xie
Keyword(s):  
Parallel Computing ◽  
Data Processing ◽  
Point Cloud ◽  
Large Scale ◽  
Point Cloud Data ◽  
Point Model ◽  
Cloud Data ◽  
Data Processing Method ◽  
Low Efficiency ◽  
Cloud Data Processing

For the construction of large-scale surface features 3D point model, a large number of point cloud data processing calculations is needed. Previous model construction calculation was treated non-parallel manner successively and mostly with one by one point cloud. This data processing method is complex, low efficiency and requires vast computing resource. Accordance with the BSP parallel computing ideas, we design a point cloud data modeling algorithm based on BSP and build a Hama parallel computing cluster consisted of ordinary PCs. The results indicate that, large-scale 3D point model BSP construction algorithm can improve the efficiency of modeling calculations and reduce computing resources requirements for processing construction computing.

scholarly journals A 3D Surface Reconstruction Method for Large-Scale Point Cloud Data

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Baoyun Guo ◽  
Jiawen Wang ◽  
Xiaobin Jiang ◽  
Cailin Li ◽  
Benya Su ◽  
...  
Keyword(s):  
Parallel Computing ◽  
Surface Reconstruction ◽  
Point Cloud ◽  
Large Scale ◽  
Divide And Conquer ◽  
Point Cloud Data ◽  
Cloud Data ◽  
3D Surface Reconstruction ◽  
3D Surface ◽  
Scale Point

Due to the memory limitation and lack of computing power of consumer level computers, there is a need for suitable methods to achieve 3D surface reconstruction of large-scale point cloud data. A method based on the idea of divide and conquer approaches is proposed. Firstly, the kd-tree index was created for the point cloud data. Then, the Delaunay triangulation algorithm of multicore parallel computing was used to construct the point cloud data in the leaf nodes. Finally, the complete 3D mesh model was realized by constrained Delaunay tetrahedralization based on piecewise linear system and graph cut. The proposed method performed surface reconstruction on the point cloud in the multicore parallel computing architecture, in which memory release and reallocation were implemented to reduce the memory occupation and improve the running efficiency while ensuring the quality of the triangular mesh. The proposed algorithm was compared with two classical surface reconstruction algorithms using multigroup point cloud data, and the applicability experiment of the algorithm was carried out; the results verify the effectiveness and practicability of the proposed approach.

Thermal-Scalable 3D Parallel-Heat-Sinking Integration Methodology: Key SoC Technology for Large-Scale Parallel Computing

2011 ◽  
Vol 34 (4) ◽  
pp. 717-728
Author(s):  
Zu-Ying LUO ◽  
Yin-He HAN ◽  
Guo-Xing ZHAO ◽  
Xian-Chuan YU ◽  
Ming-Quan ZHOU
Keyword(s):  
Parallel Computing ◽  
Large Scale ◽  
Heat Sinking

Deep Learning-Based Classification of Large-Scale Airborne LiDAR Point Cloud

2021 ◽  
pp. 1-15
Author(s):  
Mathieu Turgeon-Pelchat ◽  
Samuel Foucher ◽  
Yacine Bouroubi
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Large Scale ◽  
Airborne Lidar

scholarly journals Interactive Exploration of Large-Scale UI Datasets with Design Maps

2021 ◽  
Author(s):  
Luis A Leiva ◽  
Asutosh Hota ◽  
Antti Oulasvirta
Keyword(s):  
User Interface ◽  
Point Cloud ◽  
Large Scale ◽  
Mobile Apps ◽  
Online Resources ◽  
Design Tools ◽  
Support Design ◽  
Design Exploration ◽  
Clustering Techniques ◽  
Cloud Points

Abstract Designers are increasingly using online resources for inspiration. How to best support design exploration without compromising creativity? We introduce and study Design Maps, a class of point-cloud visualizations that makes large user interface datasets explorable. Design Maps are computed using dimensionality reduction and clustering techniques, which we analyze thoroughly in this paper. We present concepts for integrating Design Maps into design tools, including interactive visualization, local neighborhood exploration and functionality to integrate existing solutions to the design at hand. These concepts were implemented in a wireframing tool for mobile apps, which was evaluated with actual designers performing realistic tasks. Overall, designers find Design Maps supporting their creativity (avg. CSI score of 74/100) and indicate that the maps producing consistent whitespacing within cloud points are the most informative ones.

scholarly journals FPS-Net: A convolutional fusion network for large-scale LiDAR point cloud segmentation

2021 ◽  
Vol 176 ◽  
pp. 237-249
Author(s):  
Aoran Xiao ◽  
Xiaofei Yang ◽  
Shijian Lu ◽  
Dayan Guan ◽  
Jiaxing Huang
Keyword(s):  
Point Cloud ◽  
Large Scale ◽  
Point Cloud Segmentation
Sign in / Sign up

Export Citation Format

Share Document