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 Comparison of 2D and 3D wall reconstruction algorithms from point cloud data for as-built BIM

2020 ◽  
Vol 25 ◽  
pp. 173-192 ◽  
Author(s):  
Maarten Bassier ◽  
Meisam Yousefzadeh ◽  
Maarten Vergauwen
Keyword(s):  
Point Cloud ◽  
Real Life ◽  
Reconstruction Algorithm ◽  
Reconstruction Method ◽  
Point Cloud Data ◽  
Empirical Comparison ◽  
Ongoing Research ◽  
Cloud Data ◽  
2D And 3D ◽  
Wall Geometry

As-built Building Information Models (BIMs) are becoming increasingly popular in the Architectural, Engineering, Construction, Owner and Operator (AECOO) industry. These models reflect the state of the building up to as-built conditions. The production of these models for existing buildings with no prior BIM includes the segmentation and classification of point cloud data and the reconstruction of the BIM objects. The automation of this process is a must since the manual Scan-to-BIM procedure is both time-consuming and error prone. However, the automated reconstruction from point cloud data is still ongoing research with both 2D and 3D approaches being proposed. There currently is a gap in the literature concerning the quality assessment of the created entities. In this research, we present the empirical comparison of both strategies with respect to existing specifications. A 3D and a 2D reconstruction method are implemented and tested on a real life test case. The experiments focus on the reconstruction of the wall geometry from unstructured point clouds as it forms the basis of the model. Both presented approaches are unsupervised methods that segment, classify and create generic wall elements. The first method operates on the 3D point cloud itself and consists of a general approach for the segmentation and classification and a class-specific reconstruction algorithm for the wall geometry. The point cloud is first segmented into planar clusters, after which a Random Forests classifier is used with geometric and contextual features for the semantic labelling. The final wall geometry is created based on the 3D point clusters representing the walls. The second method is an efficient Manhattan-world scene reconstruction algorithm that simultaneously segments and classifies the point cloud based on point feature histograms. The wall reconstruction is considered an instance of image segmentation by representing the data as 2D raster images. Both methods have promising results towards the reconstruction of wall geometry of multi-story buildings. The experiments report that over 80% of the walls were correctly segmented by both methods. Furthermore, the reconstructed geometry is conform Level-of-Accuracy 20 for 88% of the data by the first method and for 55% by the second method despite the Manhattan-world scene assumption. The empirical comparison showcases the fundamental differences in both strategies and will support the further development of these methods.

scholarly journals A local-optimizing surface reconstruction method based on point cloud data

2020 ◽  
Vol 1605 ◽  
pp. 012065
Author(s):  
Jianwei Ma ◽  
Zhao Liu ◽  
Jiawei Li ◽  
Wenhao Du ◽  
Ziwen Qu ◽  
...  
Keyword(s):  
Surface Reconstruction ◽  
Point Cloud ◽  
Reconstruction Method ◽  
Point Cloud Data ◽  
Cloud Data

3D Model Registration Based on Feature Extraction

2011 ◽  
Vol 299-300 ◽  
pp. 1091-1094 ◽  
Author(s):  
Jiang Zhu ◽  
Yuichi Takekuma ◽  
Tomohisa Tanaka ◽  
Yoshio Saito
Keyword(s):  
Feature Extraction ◽  
Point Cloud ◽  
3D Model ◽  
Point Cloud Data ◽  
Coordinate Systems ◽  
High Precision Measurement ◽  
Registration Method ◽  
Cloud Data ◽  
Cad System ◽  
Complicated Model

Currently, design and processing of complicated model are enabled by the progress of the CAD/CAM system. In shape measurement, high precision measurement is performed using CMM. In order to evaluate the machined part, the designed model made by CAD system the point cloud data provided by the measurement system are analyzed and compared. Usually, the designed CAD model and measured point cloud data are made in the different coordinate systems, it is necessary to register those models in the same coordinate system for evaluation. In this research, a 3D model registration method based on feature extraction and iterative closest point (ICP) algorithm is proposed. It could efficiently and accurately register two models in different coordinate systems, and effectively avoid the problem of localized solution.

scholarly journals Scattered Point Cloud Data Reconstruction Algorithm Based on Local Convexity

2020 ◽  
Author(s):  
Sorush Niknamian
Keyword(s):  
Point Cloud ◽  
Reconstruction Algorithm ◽  
Large Error ◽  
Small Error ◽  
Data Reconstruction ◽  
Point Cloud Data ◽  
Local Convexity ◽  
Cloud Data ◽  
Time Consumption ◽  
Local Connection

Point cloud data reconstruction is the basis of point cloud data processing. The reconstruction effect has a great impact on application. For the problems of low precision, large error, and high time consumption of the current scattered point cloud data reconstruction algorithm, a new algorithm of scattered point cloud data reconstruction based on local convexity is proposed in this paper. Firstly, according to surface variation based on local outlier factor (SVLOF), the noise points of point cloud data are divided into near outlier and far outlier, and filtered for point cloud data preprocessing. Based on this, the algorithm based on local convexity is improved. The method of constructing local connection point set is used to replace triangulation to analyze the relationship of neighbor points. The connection part identification method is used for data reconstruction. Experimental results show that, the proposed method can reconstruct the scattered point cloud data accurately, with high precision, small error and low time consumption.

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