scholarly journals Fast Cylinder Shape Matching Using Random Sample Consensus in Large Scale Point Cloud

2019 ◽  
Vol 9 (5) ◽  
pp. 974 ◽  
Author(s):  
Young-Hoon Jin ◽  
Won-Hyung Lee
Keyword(s):  
Random Sample ◽  
Point Cloud ◽  
Large Scale ◽  
Shape Matching ◽  
Principal Component ◽  
Point Clouds ◽  
Cylinder Axis ◽  
Normal Estimation ◽  
Scale Point ◽  
Random Sample Consensus

In this paper, an algorithm is proposed that can perform cylinder type matching faster than the existing method in point clouds that represent space. The existing matching method uses Hough transform and completes the matching through preprocessing such as noise filtering, normal estimation, and segmentation. The proposed method completes the matching through the methodology of random sample consensus (RANSAC) and principal component analysis (PCA). Cylindrical pipe estimation is based on two mathematical models that compute the parameters and combine the results to predict spheres and lines. RANSAC fitting computes the center and radius of the sphere, which can be the radius of the cylinder axis and finds straight and curved areas through PCA. This allows fast matching without normal estimation and segmentation. Linear and curved regions are distinguished by a discriminant using eigenvalues. The linear region is the sum of the vectors of linear candidates, and the curved region is matched by a Catmull–Rom spline. The proposed method is expected to improve the work efficiency of the reverse design by matching linear and curved cylinder estimation without vertical/horizontal constraint and segmentation. It is also more than 10 times faster while maintaining the accuracy of the conventional method.

scholarly journals OPACITY-BASED EDGE HIGHLIGHTING FOR TRANSPARENT VISUALIZATION OF 3D SCANNED POINT CLOUDS

2020 ◽  
Vol V-2-2020 ◽  
pp. 373-380
Author(s):  
K. Kawakami ◽  
K. Hasegawa ◽  
L. Li ◽  
H. Nagata ◽  
M. Adachi ◽  
...  
Keyword(s):  
Real World ◽  
Point Cloud ◽  
Large Scale ◽  
3D Structure ◽  
Point Clouds ◽  
High Quality ◽  
3D Images ◽  
3D Structures ◽  
3D Objects ◽  
Scale Point

Abstract. The recent development of 3D scanning technologies has made it possible to quickly and accurately record various 3D objects in the real world. The 3D scanned data take the form of large-scale point clouds, which describe complex 3D structures of the target objects and the surrounding scenes. The complexity becomes significant in cases that a scanned object has internal 3D structures, and the acquired point cloud is created by merging the scanning results of both the interior and surface shapes. To observe the whole 3D structure of such complex point-based objects, the point-based transparent visualization, which we recently proposed, is useful because we can observe the internal 3D structures as well as the surface shapes based on high-quality see-through 3D images. However, transparent visualization sometimes shows us too much information so that the generated images become confusing. To address this problem, in this paper, we propose to combine “edge highlighting” with transparent visualization. This combination makes the created see-through images quite understandable because we can highlight the 3D edges of visualized shapes as high-curvature areas. In addition, to make the combination more effective, we propose a new edge highlighting method applicable to 3D scanned point clouds. We call the method “opacity-based edge highlighting,” which appropriately utilizes the effect of transparency to make the 3D edge regions look clearer. The proposed method works well for both sharp (high-curvature) and soft (low-curvature) 3D edges. We show several experiments that demonstrate our method’s effectiveness by using real 3D scanned point clouds.

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.

Registration of Point-Clouds from Terrestrial and Portable Laser Scanners

2016 ◽  
Vol 10 (2) ◽  
pp. 163-171 ◽  
Author(s):  
Takuma Watanabe ◽  
◽  
Takeru Niwa ◽  
Hiroshi Masuda ◽  
Keyword(s):  
Point Cloud ◽  
Short Range ◽  
Large Scale ◽  
Laser Scanner ◽  
Point Clouds ◽  
Limited Region ◽  
Registration Method ◽  
Laser Scanners ◽  
Scale Point ◽  
Candidate Regions

We proposed a registration method for aligning short-range point-clouds captured using a portable laser scanner (PLS) to a large-scale point-cloud captured using a terrestrial laser scanner (TLS). As a PLS covers a very limited region, it often fails to provide sufficient features for registration. In our method, the system analyzes large-scale point-clouds captured using a TLS and indicates candidate regions to be measured using a PLS. When the user measures a suggested region, the system aligns the captured short-range point-cloud to the large-scale point-cloud. Our experiments show that the registration method can adequately align point-clouds captured using a TLS and a PLS.

Special Issue on Large-Scale Point Cloud Processing

2018 ◽  
Vol 12 (3) ◽  
pp. 327-327
Author(s):  
Hiroshi Masuda ◽  
Hiroaki Date
Keyword(s):  
Point Cloud ◽  
Large Scale ◽  
Point Clouds ◽  
Special Issue ◽  
New Techniques ◽  
Accuracy Measurement ◽  
Cloud Processing ◽  
Point Cloud Processing ◽  
Laser Scanners ◽  
Scale Point

Recently, terrestrial laser scanners have been significantly improved in terms of accuracy, measurement distance, measurement speed, and resolution. They enable us to capture dense 3D point clouds of large-scale objects and fields, such as factories, engineering plants, large equipment, and transport ships. In addition, the mobile mapping system, which is a vehicle equipped with laser scanners and GPSs, can be used for capturing large-scale point clouds from a wide range of roads, buildings, and roadside objects. Large-scale point clouds are useful in a variety of applications, such as renovation and maintenance of facilities, engineering simulation, asset management, and 3D mapping. To realize these applications, new techniques must be developed for processing large-scale point clouds. So far, point processing has been studied mainly for relatively small objects in the field of computer-aided design and computer graphics. However, in recent years, the application areas of point clouds are not limited to conventional domains, but also include manufacturing, civil engineering, construction, transportation, forestry, and so on. This is because the state-of-the-art laser scanner can be used to represent large objects or fields as dense point clouds. We believe that discussing new techniques and applications related to large-scale point clouds beyond the boundaries of traditional academic fields is very important.This special issue addresses the latest research advances in large-scale point cloud processing. This covers a wide area of point processing, including shape reconstruction, geometry processing, object recognition, registration, visualization, and applications. The papers will help readers explore and share their knowledge and experience in technologies and development techniques.All papers were refereed through careful peer reviews. We would like to express our sincere appreciation to the authors for their submissions and to the reviewers for their invaluable efforts for ensuring the success of this special issue.

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 Matching Point Clouds with STL Models by Using the Principle Component Analysis and a Decomposition into Geometric Primitives

2021 ◽  
Vol 11 (5) ◽  
pp. 2268
Author(s):  
Erika Straková ◽  
Dalibor Lukáš ◽  
Zdenko Bobovský ◽  
Tomáš Kot ◽  
Milan Mihola ◽  
...  
Keyword(s):  
Point Cloud ◽  
Principal Component ◽  
Point Clouds ◽  
Component Analysis ◽  
Language Models ◽  
Computational Time ◽  
Dominant Eigenvalue ◽  
Eigenvalues And Eigenvectors ◽  
Correct Model ◽  
Geometric Primitives

While repairing industrial machines or vehicles, recognition of components is a critical and time-consuming task for a human. In this paper, we propose to automatize this task. We start with a Principal Component Analysis (PCA), which fits the scanned point cloud with an ellipsoid by computing the eigenvalues and eigenvectors of a 3-by-3 covariant matrix. In case there is a dominant eigenvalue, the point cloud is decomposed into two clusters to which the PCA is applied recursively. In case the matching is not unique, we continue to distinguish among several candidates. We decompose the point cloud into planar and cylindrical primitives and assign mutual features such as distance or angle to them. Finally, we refine the matching by comparing the matrices of mutual features of the primitives. This is a more computationally demanding but very robust method. We demonstrate the efficiency and robustness of the proposed methodology on a collection of 29 real scans and a database of 389 STL (Standard Triangle Language) models. As many as 27 scans are uniquely matched to their counterparts from the database, while in the remaining two cases, there is only one additional candidate besides the correct model. The overall computational time is about 10 min in MATLAB.

scholarly journals Geological Modeling Method Based on the Normal Dynamic Estimation of Sparse Point Clouds

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1819
Author(s):  
Tiandong Shi ◽  
Deyun Zhong ◽  
Liguan Wang
Keyword(s):  
Point Cloud ◽  
Minimum Spanning Tree ◽  
Point Clouds ◽  
Original Method ◽  
Modeling Method ◽  
Geological Modeling ◽  
Improved Method ◽  
Normal Estimation ◽  
Sampling Points ◽  
Correct Point

The effect of geological modeling largely depends on the normal estimation results of geological sampling points. However, due to the sparse and uneven characteristics of geological sampling points, the results of normal estimation have great uncertainty. This paper proposes a geological modeling method based on the dynamic normal estimation of sparse point clouds. The improved method consists of three stages: (1) using an improved local plane fitting method to estimate the normals of the point clouds; (2) using an improved minimum spanning tree method to redirect the normals of the point clouds; (3) using an implicit function to construct a geological model. The innovation of this method is an iterative estimation of the point cloud normal. The geological engineer adjusts the normal direction of some point clouds according to the geological law, and then the method uses these correct point cloud normals as a reference to estimate the normals of all point clouds. By continuously repeating the iterative process, the normal estimation result will be more accurate. Experimental results show that compared with the original method, the improved method is more suitable for the normal estimation of sparse point clouds by adjusting normals, according to prior knowledge, dynamically.

Facet Segmentation-Based Line Segment Extraction for Large-Scale Point Clouds

2017 ◽  
Vol 55 (9) ◽  
pp. 4839-4854 ◽  
Author(s):  
Yangbin Lin ◽  
Cheng Wang ◽  
Bili Chen ◽  
Dawei Zai ◽  
Jonathan Li
Keyword(s):  
Line Segment ◽  
Large Scale ◽  
Point Clouds ◽  
Scale Point

A Novel Coding Scheme for Large-Scale Point Cloud Sequences Based on Clustering and Registration

2021 ◽  
pp. 1-13
Author(s):  
Xuebin Sun ◽  
Yuxiang Sun ◽  
Weixun Zuo ◽  
Shing Shin Cheng ◽  
Ming Liu
Keyword(s):  
Point Cloud ◽  
Large Scale ◽  
Coding Scheme ◽  
Scale Point
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