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

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.

Automobile Panel Reverse Engineering Design Based on NURBS Surface Reconstruction Technique

2011 ◽  
Vol 128-129 ◽  
pp. 1341-1344
Author(s):  
Hong Yuan Zhang ◽  
Peng He
Keyword(s):  
Engineering Design ◽  
Surface Reconstruction ◽  
Point Cloud ◽  
Reconstruction Technique ◽  
Point Cloud Data ◽  
Data Sampling ◽  
Nurbs Surface ◽  
Cloud Data ◽  
Fast Speed ◽  
Automobile Panel

This paper takes automobile panel as the study object, and adopts non-contact 3-D scanner to obtain the point cloud data of the automobile panel for point cloud data sampling and noise reduction processing. It obtains NURBS surface fitting through detecting of curvature and grid. Reverse design can improve the product prototype in a fast speed and provide an important way of automobile panel development.

Reverse Engineering Design of Vehicle Rear Axle Based on CATIA

2014 ◽  
Vol 697 ◽  
pp. 298-301
Author(s):  
Yong Qing Xia ◽  
Jie Zhang
Keyword(s):  
Reverse Engineering ◽  
Engineering Design ◽  
Surface Reconstruction ◽  
Point Cloud ◽  
Rear Axle ◽  
Feasible Solution ◽  
Point Cloud Data ◽  
Engineering Technology ◽  
General Process ◽  
Cloud Data

Reverse engineering technology has been widely used in engineering design field. This paper proposed a CATIA-modeling-based reverse engineering technology for vehicle rear axle design. The general process for reverse engineering includes the pretreatment of the point cloud data, the establishment of the grid, the surface reconstruction of parts, the analysis of curved surface, and so on. Finally, the paper puted forward practical and feasible solution to the problem.

scholarly journals ACCURACY OF 3D RECONSTRUCTION IN AN ILLUMINATION DOME

2016 ◽  
Vol XLI-B5 ◽  
pp. 69-76 ◽  
Author(s):  
Lindsay MacDonald ◽  
Isabella Toschi ◽  
Erica Nocerino ◽  
Mona Hess ◽  
Fabio Remondino ◽  
...  
Keyword(s):  
Surface Reconstruction ◽  
High Frequency ◽  
Point Cloud ◽  
Laser Scanner ◽  
Low Frequency ◽  
Photometric Stereo ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Digital Elevation ◽  
Elevation Model

The accuracy of 3D surface reconstruction was compared from image sets of a Metric Test Object taken in an illumination dome by two methods: photometric stereo and improved structure-from-motion (SfM), using point cloud data from a 3D colour laser scanner as the reference. Metrics included pointwise height differences over the digital elevation model (DEM), and 3D Euclidean differences between corresponding points. The enhancement of spatial detail was investigated by blending high frequency detail from photometric normals, after a Poisson surface reconstruction, with low frequency detail from a DEM derived from SfM.

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