Technology of freeform surface reconstruction from laser scanning point cloud based on nonuniform B-spline

2006 ◽  
Author(s):  
Yingfu Guo ◽  
Zhuangde Jiang ◽  
Xiaoqiang Wang ◽  
Bing Li
Keyword(s):  
Surface Reconstruction ◽  
Point Cloud ◽  
Laser Scanning ◽  
Freeform Surface ◽  
B Spline

Measured Scan Data Reduction Combination Algorithm Based on Scan Line

2014 ◽  
Vol 651-653 ◽  
pp. 2335-2338
Author(s):  
Shi Gang Wang ◽  
Yong Yan ◽  
Feng Juan Wang
Keyword(s):  
Surface Reconstruction ◽  
Point Cloud ◽  
Minimum Distance ◽  
Laser Scanning ◽  
Hot Spot ◽  
Point Cloud Data ◽  
3D Laser Scanning ◽  
Cloud Data ◽  
Scan Data ◽  
Cloud Data Processing

The 3D laser scanning technology is a hot spot in developed measuring in recent years. In the surface reconstruction of reverse engineering, the 3D laser scanning point cloud data is too large, and is not conducive to the computation, storage and surface reconstruction. After understanding the research status of the point cloud data processing streamline method at home and abroad, and through the analysis of minimum distance algorithm and the angle-chord height combined code method applicable to engineering characteristics, at the same time, the combination algorithm, which is based on the minimum distance algorithm and the angle-chord height combined code method, is proposed to simplify the point cloud data. The scanning point cloud is simplified by using matlab line by line.

Surface Reconstruction Based on the 3D Laser Scanner

2012 ◽  
Vol 215-216 ◽  
pp. 656-659
Author(s):  
Le Yang Chen
Keyword(s):  
Reverse Engineering ◽  
Surface Reconstruction ◽  
Point Cloud ◽  
Laser Scanning ◽  
Laser Scanner ◽  
3D Laser Scanning ◽  
Cloud Processing ◽  
Point Cloud Processing ◽  
Key Technologies ◽  
Rapid Scanning

3D laser scanning is one of the key technologies of reverse engineering. Digital point cloud is produced by the rapid scanning technology. Some technology about reverse engineering is introduced in this thesis. The curved surface can be generated by the point cloud processing, when the point cloud can be processed by the software called Geomagic Studio.

Study on Reconstruction Property of Revolved Surface Based on B-Spline

2014 ◽  
Vol 644-650 ◽  
pp. 1674-1677
Author(s):  
Ruo Can Sun ◽  
Dan Liu ◽  
Ji Qing Zhao ◽  
Ying Ping Qian ◽  
Guo Feng Yi
Keyword(s):  
Surface Reconstruction ◽  
Cross Section ◽  
Point Cloud ◽  
Control Points ◽  
Surface Point ◽  
Scanning Angle ◽  
Factors Affecting ◽  
B Spline ◽  
Spline Curve ◽  
Cloud Density

B-spline curve parameters were estimated by the least squares. The influences of the scanning angle to point cloud density was discussed. In the case of scanning distance and horizontal being sure, the smaller the vertical scanning angle, the larger point cloud density, it can reflect the characteristics of the surface cross-section more accurately. The factors affecting the accuracy of the surface reconstruction were discussed. The surface fitting order has a greater degree of influence on the accuracy of surface reconstruction. When the surface order was 4 and the number of control points was 75, the surface-point cloud deviation was 0.188mm by the segmented fitting.

Assessment of automatic segmentation accuracy with various point cloud density

2020 ◽  
Vol 961 (7) ◽  
pp. 47-55
Author(s):  
A.G. Yunusov ◽  
A.J. Jdeed ◽  
N.S. Begliarov ◽  
M.A. Elshewy
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Reference Data ◽  
Automatic Segmentation ◽  
Geometric Accuracy ◽  
Segmentation Method ◽  
Segmentation Methods ◽  
Segmentation Accuracy ◽  
Number Of Segments ◽  
Cloud Density

Laser scanning is considered as one of the most useful and fast technologies for modelling. On the other hand, the size of scan results can vary from hundreds to several million points. As a result, the large volume of the obtained clouds leads to complication at processing the results and increases the time costs. One way to reduce the volume of a point cloud is segmentation, which reduces the amount of data from several million points to a limited number of segments. In this article, we evaluated effect on the performance, the accuracy of various segmentation methods and the geometric accuracy of the obtained models at density changes taking into account the processing time. The results of our experiment were compared with reference data in a form of comparative analysis. As a conclusion, some recommendations for choosing the best segmentation method were proposed.

scholarly journals Linear-Based Incremental Co-Registration of MLS and Photogrammetric Point Clouds

2021 ◽  
Vol 13 (11) ◽  
pp. 2195
Author(s):  
Shiming Li ◽  
Xuming Ge ◽  
Shengfu Li ◽  
Bo Xu ◽  
Zhendong Wang
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Registration Method ◽  
Cloud Data ◽  
Linear Feature ◽  
Data Registration ◽  
Source Point ◽  
Urban Remote Sensing ◽  
Single Data

Today, mobile laser scanning and oblique photogrammetry are two standard urban remote sensing acquisition methods, and the cross-source point-cloud data obtained using these methods have significant differences and complementarity. Accurate co-registration can make up for the limitations of a single data source, but many existing registration methods face critical challenges. Therefore, in this paper, we propose a systematic incremental registration method that can successfully register MLS and photogrammetric point clouds in the presence of a large number of missing data, large variations in point density, and scale differences. The robustness of this method is due to its elimination of noise in the extracted linear features and its 2D incremental registration strategy. There are three main contributions of our work: (1) the development of an end-to-end automatic cross-source point-cloud registration method; (2) a way to effectively extract the linear feature and restore the scale; and (3) an incremental registration strategy that simplifies the complex registration process. The experimental results show that this method can successfully achieve cross-source data registration, while other methods have difficulty obtaining satisfactory registration results efficiently. Moreover, this method can be extended to more point-cloud sources.

scholarly journals Parametric Surface Modelling for Tea Leaf Point Cloud Based on Non-Uniform Rational Basis Spline Technique

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1304
Author(s):  
Wenchao Wu ◽  
Yongguang Hu ◽  
Yongzong Lu
Keyword(s):  
Point Cloud ◽  
Principal Component ◽  
Point Clouds ◽  
Parametric Surface ◽  
B Spline ◽  
Surface Modelling ◽  
Modelling Method ◽  
3D Architecture ◽  
Tea Leaf ◽  
Fitting Error

Plant leaf 3D architecture changes during growth and shows sensitive response to environmental stresses. In recent years, acquisition and segmentation methods of leaf point cloud developed rapidly, but 3D modelling leaf point clouds has not gained much attention. In this study, a parametric surface modelling method was proposed for accurately fitting tea leaf point cloud. Firstly, principal component analysis was utilized to adjust posture and position of the point cloud. Then, the point cloud was sliced into multiple sections, and some sections were selected to generate a point set to be fitted (PSF). Finally, the PSF was fitted into non-uniform rational B-spline (NURBS) surface. Two methods were developed to generate the ordered PSF and the unordered PSF, respectively. The PSF was firstly fitted as B-spline surface and then was transformed to NURBS form by minimizing fitting error, which was solved by particle swarm optimization (PSO). The fitting error was specified as weighted sum of the root-mean-square error (RMSE) and the maximum value (MV) of Euclidean distances between fitted surface and a subset of the point cloud. The results showed that the proposed modelling method could be used even if the point cloud is largely simplified (RMSE < 1 mm, MV < 2 mm, without performing PSO). Future studies will model wider range of leaves as well as incomplete point cloud.

scholarly journals Revealing Changes in the Stem Form and Volume Allocation in Diverse Boreal Forests Using Two-Date Terrestrial Laser Scanning

Forests ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 835
Author(s):  
Ville Luoma ◽  
Tuomas Yrttimaa ◽  
Ville Kankare ◽  
Ninni Saarinen ◽  
Jiri Pyörälä ◽  
...  
Keyword(s):  
Tree Growth ◽  
Point Cloud ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
Stem Volume ◽  
Accurate Information ◽  
Stem Form ◽  
Monitoring Period ◽  
Significant Difference

Tree growth is a multidimensional process that is affected by several factors. There is a continuous demand for improved information on tree growth and the ecological traits controlling it. This study aims at providing new approaches to improve ecological understanding of tree growth by the means of terrestrial laser scanning (TLS). Changes in tree stem form and stem volume allocation were investigated during a five-year monitoring period. In total, a selection of attributes from 736 trees from 37 sample plots representing different forest structures were extracted from taper curves derived from two-date TLS point clouds. The results of this study showed the capability of point cloud-based methods in detecting changes in the stem form and volume allocation. In addition, the results showed a significant difference between different forest structures in how relative stem volume and logwood volume increased during the monitoring period. Along with contributing to providing more accurate information for monitoring purposes in general, the findings of this study showed the ability and many possibilities of point cloud-based method to characterize changes in living organisms in particular, which further promote the feasibility of using point clouds as an observation method also in ecological studies.

Sign in / Sign up

Export Citation Format

Share Document