A CAD Modeling Method of Dental Surface Based on Scanning Data

2012 ◽  
Vol 151 ◽  
pp. 111-115
Author(s):  
Li Cheng Fan ◽  
Feng Feng Zhang
Keyword(s):  
Point Cloud ◽  
Cnc Machining ◽  
Tooth Surface ◽  
Iteration Algorithm ◽  
Point Cloud Data ◽  
Scanning Method ◽  
Cad Modeling ◽  
Cloud Data ◽  
Data Registration ◽  
3D Data

The measurement of the teeth surface and the CAD modeling of the point cloud data are the key basics for the following CNC machining, and the complete data can only be obtained through multi-perspective scanning method. Using ICP iteration algorithm that based on point-to-line to the multi-perspective scanning data, specific to the features of layering scanning, retrograde the 3D data registration to 2D planar registration, and provide the cutting and splicing algorithm for registered tooth data, obtain precise and integrated tooth surface point cloud data, which serves as the CAD model for the following CNC machining.

METHOD OF REGISTRATION FOR 3D FACE POINT CLOUD DATA

2015 ◽  
Vol 75 (2) ◽  
Author(s):  
Mohd Kufaisal Mohd Sidik ◽  
Mohd Shahrizal Sunar ◽  
Muhamad Najib Zamri
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Human Head ◽  
Point Of View ◽  
Front Face ◽  
Point Cloud Data ◽  
3D Face ◽  
Cloud Data ◽  
Data Registration ◽  
The Face

This paper analyzes the techniques that can be used to perform point cloud data registration for a human face. We found that there is a limitation in full scale viewing on the input data taken from 3D camera which is only represented the front face of a man as the point of view of a camera. This has caused a hole on the surface that is not filled with the point cloud data. This research is done by mapping the retrieved point cloud to the surface of the face template of the human head. By using Coherent Point Drift (CPD) algorithm which is one of the non-rigid registration techniques, the analysis has been done and it shows that the mapping of points for a three-dimensional (3D) face is not done properly where there are some surfaces work well and certain points spread into the wrong area. Consequently, it has resulted in registration failure occurrences due to the concentration of the points which is focusing on the face only.

The Application of 3D Laser Scanning Method in the Measurement of Pressure Vessel

2017 ◽  
Vol 865 ◽  
pp. 595-598
Author(s):  
Hui Zeng Yin ◽  
Xin Wei Yang ◽  
Rui Lan Tian ◽  
Xiu Zhi Sui
Keyword(s):  
Pressure Vessel ◽  
Point Cloud ◽  
Laser Scanning ◽  
Convex Surface ◽  
Measuring Method ◽  
Point Cloud Data ◽  
3D Laser Scanning ◽  
Scanning Method ◽  
Cloud Data ◽  
Dangerous Goods

Pressure vessel is widely used in the industrial engineering. Many materials in pressure vessel are inflammable and explosive dangerous goods. If the accident happens, great harm will be done to the lives and properties of people. Some common methods for studying pressure vessel have obvious drawbacks. 3D laser scanning method uses non-contact measuring method and can directly obtain the point cloud data of the mass surface which can be used to reconstruct any convex surface. According to the advantages of 3D laser scanning method, in this paper, it is introduced to measure the dimensions of flanges in pressure vessel. The experimental results obtained have little errors, which certify that 3D laser scanning method can be used to measure the dimensions of flanges and further study the characteristics of pressure vessel.

Three-Dimensional Reconstruction of Maize Plants and Extraction of Phenotypic Parameters Based On Laser Point Cloud Data

2021 ◽  
Author(s):  
Chengxin Ju ◽  
Yuanyuan Zhao ◽  
Fengfeng Wu ◽  
Rui Li ◽  
Tianle Yang ◽  
...  
Keyword(s):  
3D Reconstruction ◽  
Plant Height ◽  
Point Cloud ◽  
Agricultural Research ◽  
Three Dimensional ◽  
Laser Scanner ◽  
Point Cloud Data ◽  
Scanning Method ◽  
Cloud Data ◽  
Maize Plants

Abstract Background: Three-dimensional (3D) laser scanning technology could rapidly extract the surface geometric features of maize plants to achieve non-destructive monitoring of maize phenotypes. However, extracting the phenotypic parameters of maize plants based on laser point cloud data is challenging.Methods: In this paper, a rotational scanning method was used to collect the data of potted maize point cloud from different perspectives by using a laser scanner. Maize point cloud data were grid-reconstructed and aligned based on greedy projection triangulation algorithm and iterative closest point (ICP) algorithm, and the random sampling consistency algorithm was used to segment the stem and leaf point clouds of single maize plant to obtain the plant height and leaf parameters.Results: The results showed that the R2 between the predicted plant height and the measured plant height was above 0.95, and the R2 of the predicted leaf length, leaf width and leaf area were 0.938, 0878 and 0.956 respectively when compared with the measured values.Conclusions: The 3D reconstruction of maize plants using the laser scanner showed a good performance, and the phenotypic parameters obtained based on the reconstructed 3D model had high accuracy. The results were helpful to the practical application of plant 3D reconstruction and provided guidance for plant parameter acquisition and theoretical methods for intelligent agricultural research.

A Novel Pre-Processing Approach of Point Cloud in Reverse Engineering

2013 ◽  
Vol 331 ◽  
pp. 631-635
Author(s):  
Ci Zhang ◽  
Guo Fan Hu ◽  
Xu Bing Chen
Keyword(s):  
Reverse Engineering ◽  
Point Cloud ◽  
Triangular Grid ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Engineering Data ◽  
Processing Approach ◽  
3D Data ◽  
Cloud Data Processing

In reverse engineering, data pre-processing has played an increasingly important role for rebuilding the original 3D model. However, it is usually complex, time-consuming, and difficult to realize, as there are huge amounts of redundant 3D data existed in the gained point cloud. To find a solution for this issue, point cloud data processing and streamlining technologies are reviewed firstly. Secondly, a novel pre-processing approach is proposed in three steps: point cloud registration, regional 3D triangular mesh construction and point cloud filtering. And then, the projected hexagonal area and the closest projected point are defined. At last, a parabolic antenna model is employed as a case study. After pre-processing, the number of points are decreased from 4,066,282 to 449,806 under the constraint of triangular grid size h equaling to 2mm, i.e. about 1/9 size of the original point cloud. The result demonstrates its feasibility and efficiency.

Detection of wheel-set size of rail vehicle using double 2D laser displacement sensors based on point cloud data registration in frequency domain

2017 ◽  
Vol 25 (3) ◽  
pp. 616-624
Author(s):  
王 华 WANG Hua ◽  
邢春齐 XING Chun-qi ◽  
高金刚 GAO Jin-gang ◽  
张 爽 ZHANG Shuang ◽  
朱可可 ZHU Ke-ke
Keyword(s):  
Frequency Domain ◽  
Point Cloud ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Data Registration ◽  
Set Size ◽  
Rail Vehicle ◽  
Displacement Sensors

Surface Reconstruction and Nc Simulation of Auto Rearview Mirror Based on Geomagic Studio

2012 ◽  
Vol 271-272 ◽  
pp. 515-518 ◽  
Author(s):  
Huan Lin ◽  
Dong Qiang Gao ◽  
Jiang Miao Yi
Keyword(s):  
Data Processing ◽  
Surface Reconstruction ◽  
Point Cloud ◽  
Laser Scanner ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Data Registration ◽  
Nc Simulation ◽  
Cnc Programming ◽  
Rearview Mirror

The key techniques of reverse engineering include data acquisition, data processing and model reconstruction.In this paper, with the automobile rearview mirror shell for example, scan the rearview mirror shell surface by laser scanner; then carries on the data processing to point cloud data(data processing include point cloud data registration, joining together and polygon stage processing). On the basis of data processing, fitting NURBS surface by Geomagic Studio software, thus completing surface reconstruction; Finally through the NC machining simulation, gets CNC programming, and to make the rearview mirror surface reconstruction and the numerical simulation.

scholarly journals A Lightweight, Robust Exploitation System for Temporal Stacks of UAS Data: Use Case for Forward-Deployed Military or Emergency Responders

Drones ◽  
2019 ◽  
Vol 3 (1) ◽  
pp. 29 ◽  
Author(s):  
Andrew Marx ◽  
Yu-Hsi Chou ◽  
Kevin Mercy ◽  
Richard Windisch
Keyword(s):  
Urban Areas ◽  
Point Cloud ◽  
Point Clouds ◽  
Temporal Analysis ◽  
Unmanned Aerial Systems ◽  
Point Cloud Data ◽  
High Quality ◽  
Cloud Data ◽  
3D Data ◽  
Exploitation System

The availability and precision of unmanned aerial systems (UAS) permit the repeated collection of very-high quality three-dimensional (3D) data to monitor high-interest areas, such as dams, urban areas, or erosion-prone coastlines. However, challenges exist in the temporal analysis of this data, specifically in conducting change-detection analysis on the high-quality point cloud data. These files are very large in size and contain points in varying locations that do not align between scenes. These large file sizes also limit the use of this data for individuals with low computational resources, such as first responders or forward-deployed soldiers. In response, this manuscript presents an approach that aggregates data spatially into voxels to provide the user with a lightweight, web-based exploitation system coupled with a flexible backend database. The system creates a robust set of tools to analyze large temporal stacks of 3D data and reduces data size by 78%, all while being able to query the original point cloud data. This approach offers a solution for organizations analyzing high-resolution, temporal point-clouds, as well as a possible solution for operations in areas with poor computational and connectivity resources requiring high-quality, 3D data for decision support and planning.

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