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.

scholarly journals Triangulation Reconstruction for 3D Surface Based on Information Model

2016 ◽  
Vol 16 (5) ◽  
pp. 27-33
Author(s):  
Na Li ◽  
Jiquan Yang ◽  
Aiqing Guo ◽  
Yijian Liu ◽  
Hai Liu
Keyword(s):  
Surface Reconstruction ◽  
Point Cloud ◽  
Large Scale ◽  
Low Complexity ◽  
Information Model ◽  
Landslide Monitoring ◽  
Reconstruction Method ◽  
Cloud Data ◽  
3D Surface ◽  
3D Information

Abstract The aim of this paper is to address the surface reconstruction from point cloud in reverser engineering. The data was acquired through a 3D scan device and was processed as point cloud data. The points in cloud were connected to build 3D surface. The points cloud was processed in four steps to get 3D information surface. First, the subtraction scheme was used to get cover boxes ended with the set of convex was found under the convergence rule. Secondly, the points in the box were projected to the directions which were close to the normal direction method. Thirdly the overlap was avoided by using convergence rule and inner subdivision rule. Finally the information model was used to reconstruction. The method was used in landslide monitoring of Three Gorges area for 3D surface reconstruction and monitoring. The reconstruction method obtains high precision and low complexity. It is effective for large scale monitoring.

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 CLASSIFICATION OF BIG POINT CLOUD DATA USING CLOUD COMPUTING

2015 ◽  
Vol XL-3/W3 ◽  
pp. 553-557 ◽  
Author(s):  
K. Liu ◽  
J. Boehm
Keyword(s):  
Point Cloud ◽  
Large Scale ◽  
Cluster Computing ◽  
Semantic Analysis ◽  
Apache Spark ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Data Volume ◽  
Scale Point ◽  
Point Data

Point cloud data plays an significant role in various geospatial applications as it conveys plentiful information which can be used for different types of analysis. Semantic analysis, which is an important one of them, aims to label points as different categories. In machine learning, the problem is called classification. In addition, processing point data is becoming more and more challenging due to the growing data volume. In this paper, we address point data classification in a big data context. The popular cluster computing framework Apache Spark is used through the experiments and the promising results suggests a great potential of Apache Spark for large-scale point data processing.

scholarly journals Fused Transparent Visualization of Point Cloud Data and Background Photographic Image for Tangible Cultural Heritage Assets

2019 ◽  
Vol 8 (8) ◽  
pp. 343 ◽  
Author(s):  
Li ◽  
Hasegawa ◽  
Nii ◽  
Tanaka
Keyword(s):  
Cultural Heritage ◽  
Point Cloud ◽  
Laser Scanning ◽  
Large Scale ◽  
Three Dimensional ◽  
Point Clouds ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Photographic Image ◽  
Scale Point

Digital archiving of three-dimensional cultural heritage assets has increased the demand for visualization of large-scale point clouds of cultural heritage assets acquired by laser scanning. We proposed a fused transparent visualization method that visualizes a point cloud of a cultural heritage asset in an environment using a photographic image as the background. We also proposed lightness adjustment and color enhancement methods to deal with the reduced visibility caused by the fused visualization. We applied the proposed method to a laser-scanned point cloud of a high-valued cultural festival float with complex inner and outer structures. Experimental results demonstrate that the proposed method enables high-quality transparent visualization of the cultural asset in its surrounding environment.

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