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

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.

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

Applied Sciences ◽

10.3390/app11052268 ◽

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.

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Principal Component Analysis For ICP Pose Estimation Of Space Structures

10.32920/ryerson.14656941 ◽

2021 ◽

Author(s):

Lun H. Mark

Keyword(s):

Pose Estimation ◽

Point Cloud ◽

Principal Component ◽

Point Clouds ◽

Optimal Combination ◽

Estimation Accuracy ◽

Space Structures ◽

Complex Objects ◽

Error Norm ◽

Simulation Results

This thesis investigates how geometry of complex objects is related to LIDAR scanning with the Iterative Closest Point (ICP) pose estimation and provides statistical means to assess the pose accuracy. LIDAR scanners have become essential parts of space vision systems for autonomous docking and rendezvous. Principal Componenet Analysis based geometric constraint indices have been found to be strongly related to the pose error norm and the error of each individual degree of freedom. This leads to the development of several strategies for identifying the best view of an object and the optimal combination of localized scanned areas of the object's surface to achieve accurate pose estimation. Also investigated is the possible relation between the ICP pose estimation accuracy and the districution or allocation of the point cloud. The simulation results were validated using point clouds generated by scanning models of Quicksat and a cuboctahedron using Neptec's TriDAR scanner.

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Principal Component Analysis For ICP Pose Estimation Of Space Structures

10.32920/ryerson.14656941.v1 ◽

2021 ◽

Author(s):

Lun H. Mark

Keyword(s):

Pose Estimation ◽

Point Cloud ◽

Principal Component ◽

Point Clouds ◽

Optimal Combination ◽

Estimation Accuracy ◽

Space Structures ◽

Complex Objects ◽

Error Norm ◽

Simulation Results

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Tunnel Deformation Inspection via Global Spatial Axis Extraction from 3D Raw Point Cloud

Sensors ◽

10.3390/s20236815 ◽

2020 ◽

Vol 20 (23) ◽

pp. 6815

Author(s):

Cheng Yi ◽

Dening Lu ◽

Qian Xie ◽

Jinxuan Xu ◽

Jun Wang

Keyword(s):

Cross Sections ◽

Point Cloud ◽

Large Scale ◽

Spline Approximation ◽

Point Clouds ◽

Driving Safety ◽

Inspection Method ◽

B Spline ◽

3D Point Clouds ◽

Spatial Axis

Global inspection of large-scale tunnels is a fundamental yet challenging task to ensure the structural stability of tunnels and driving safety. Advanced LiDAR scanners, which sample tunnels into 3D point clouds, are making their debut in the Tunnel Deformation Inspection (TDI). However, the acquired raw point clouds inevitably possess noticeable occlusions, missing areas, and noise/outliers. Considering the tunnel as a geometrical sweeping feature, we propose an effective tunnel deformation inspection algorithm by extracting the global spatial axis from the poor-quality raw point cloud. Essentially, we convert tunnel axis extraction into an iterative fitting optimization problem. Specifically, given the scanned raw point cloud of a tunnel, the initial design axis is sampled to generate a series of normal planes within the corresponding Frenet frame, followed by intersecting those planes with the tunnel point cloud to yield a sequence of cross sections. By fitting cross sections with circles, the fitted circle centers are approximated with a B-Spline curve, which is considered as an updated axis. The procedure of “circle fitting and B-SPline approximation” repeats iteratively until convergency, that is, the distance of each fitted circle center to the current axis is smaller than a given threshold. By this means, the spatial axis of the tunnel can be accurately obtained. Subsequently, according to the practical mechanism of tunnel deformation, we design a segmentation approach to partition cross sections into meaningful pieces, based on which various inspection parameters can be automatically computed regarding to tunnel deformation. A variety of practical experiments have demonstrated the feasibility and effectiveness of our inspection method.

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MONITORING OF PROGRESSIVE DAMAGE IN BUILDINGS USING LASER SCAN DATA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-923-2018 ◽

2018 ◽

Vol XLII-2 ◽

pp. 923-929 ◽

Cited By ~ 7

Author(s):

I. Puente ◽

R. Lindenbergh ◽

A. Van Natijne ◽

R. Esposito ◽

R. Schipper

Keyword(s):

Point Cloud ◽

Early Stage ◽

Principal Component ◽

Point Clouds ◽

Deformation Analysis ◽

Main Concern ◽

Specific Element ◽

University Of Technology ◽

Scan Data

Vulnerability of buildings to natural and man-induced hazards has become a main concern for our society. Ensuring their serviceability, safety and sustainability is of vital importance and the main reason for setting up monitoring systems to detect damages at an early stage. In this work, a method is presented for detecting changes from laser scan data, where no registration between different epochs is needed. To show the potential of the method, a case study of a laboratory test carried out at the Stevin laboratory of Delft University of Technology was selected. The case study was a quasi-static cyclic pushover test on a two-story high unreinforced masonry structure designed to simulate damage evolution caused by cyclic loading. During the various phases, we analysed the behaviour of the masonry walls by monitoring the deformation of each masonry unit. First a plane is fitted to the selected wall point cloud, consisting of one single terrestrial laser scan, using Principal Component Analysis (PCA). Second, the segmentation of individual elements is performed. Then deformations with respect to this plane model, for each epoch and specific element, are determined by computing their corresponding rotation and cloud-to-plane distances. The validation of the changes detected within this approach is done by comparison with traditional deformation analysis based on co-registered TLS point clouds between two or more epochs of building measurements. Initial results show that the sketched methodology is indeed able to detect changes at the mm level while avoiding 3D point cloud registration, which is a main issue in computer vision and remote sensing.

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LOCALIZATION OF PATHOLOGY ON COMPLEX ARCHITECTURE BUILDING SURFACES

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-w3-617-2017 ◽

2017 ◽

Vol XLII-2/W3 ◽

pp. 617-621 ◽

Cited By ~ 1

Author(s):

A. A. Sidiropoulos ◽

K. N. Lakakis ◽

V. K. Mouza

Keyword(s):

Point Cloud ◽

Laser Scanning ◽

Principal Component ◽

Point Clouds ◽

Normal Vector ◽

Northern Greece ◽

Complex Building ◽

Building Surfaces ◽

Definition Of ◽

Complex Architecture

The technology of 3D laser scanning is considered as one of the most common methods for heritage documentation. The point clouds that are being produced provide information of high detail, both geometric and thematic. There are various studies that examine techniques of the best exploitation of this information. In this study, an algorithm of pathology localization, such as cracks and fissures, on complex building surfaces is being tested. The algorithm makes use of the points’ position in the point cloud and tries to distinguish them in two groups-patterns; pathology and non-pathology. The extraction of the geometric information that is being used for recognizing the pattern of the points is being accomplished via Principal Component Analysis (PCA) in user-specified neighborhoods in the whole point cloud. The implementation of PCA leads to the definition of the normal vector at each point of the cloud. Two tests that operate separately examine both local and global geometric criteria among the points and conclude which of them should be categorized as pathology. The proposed algorithm was tested on parts of the Gazi Evrenos Baths masonry, which are located at the city of Giannitsa at Northern Greece.

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HoPE: Horizontal Plane Extractor for Cluttered 3D Scenes

Sensors ◽

10.3390/s18103214 ◽

2018 ◽

Vol 18 (10) ◽

pp. 3214 ◽

Cited By ~ 1

Author(s):

Zhipeng Dong ◽

Yi Gao ◽

Jinfeng Zhang ◽

Yunhui Yan ◽

Xin Wang ◽

...

Keyword(s):

Horizontal Plane ◽

Point Cloud ◽

Nearest Neighbor ◽

Three Dimensional ◽

Principal Component ◽

Region Growing ◽

Point Clouds ◽

Measurement Unit ◽

Coordinate Frame ◽

3D Point Clouds

Extracting horizontal planes in heavily cluttered three-dimensional (3D) scenes is an essential procedure for many robotic applications. Aiming at the limitations of general plane segmentation methods on this subject, we present HoPE, a Horizontal Plane Extractor that is able to extract multiple horizontal planes in cluttered scenes with both organized and unorganized 3D point clouds. It transforms the source point cloud in the first stage to the reference coordinate frame using the sensor orientation acquired either by pre-calibration or an inertial measurement unit, thereby leveraging the inner structure of the transformed point cloud to ease the subsequent processes that use two concise thresholds for producing the results. A revised region growing algorithm named Z clustering and a principal component analysis (PCA)-based approach are presented for point clustering and refinement, respectively. Furthermore, we provide a nearest neighbor plane matching (NNPM) strategy to preserve the identities of extracted planes across successive sequences. Qualitative and quantitative evaluations of both real and synthetic scenes demonstrate that our approach outperforms several state-of-the-art methods under challenging circumstances, in terms of robustness to clutter, accuracy, and efficiency. We make our algorithm an off-the-shelf toolbox which is publicly available.

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ECPC-ICP: A 6D Vehicle Pose Estimation Method by Fusing the Roadside Lidar Point Cloud and Road Feature

Sensors ◽

10.3390/s21103489 ◽

2021 ◽

Vol 21 (10) ◽

pp. 3489

Author(s):

Bo Gu ◽

Jianxun Liu ◽

Huiyuan Xiong ◽

Tongtong Li ◽

Yuelong Pan

Keyword(s):

Pose Estimation ◽

Point Cloud ◽

Estimation Method ◽

Principal Component ◽

Point Clouds ◽

Target Point ◽

Test Environment ◽

Cloud Data ◽

Practical Applications ◽

The Road

In the vehicle pose estimation task based on roadside Lidar in cooperative perception, the measurement distance, angle, and laser resolution directly affect the quality of the target point cloud. For incomplete and sparse point clouds, current methods are either less accurate in correspondences solved by local descriptors or not robust enough due to the reduction of effective boundary points. In response to the above weakness, this paper proposed a registration algorithm Environment Constraint Principal Component-Iterative Closest Point (ECPC-ICP), which integrated road information constraints. The road normal feature was extracted, and the principal component of the vehicle point cloud matrix under the road normal constraint was calculated as the initial pose result. Then, an accurate 6D pose was obtained through point-to-point ICP registration. According to the measurement characteristics of the roadside Lidars, this paper defined the point cloud sparseness description. The existing algorithms were tested on point cloud data with different sparseness. The simulated experimental results showed that the positioning MAE of ECPC-ICP was about 0.5% of the vehicle scale, the orientation MAE was about 0.26°, and the average registration success rate was 95.5%, which demonstrated an improvement in accuracy and robustness compared with current methods. In the real test environment, the positioning MAE was about 2.6% of the vehicle scale, and the average time cost was 53.19 ms, proving the accuracy and effectiveness of ECPC-ICP in practical applications.

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

Applied Sciences ◽

10.3390/app9050974 ◽

2019 ◽

Vol 9 (5) ◽

pp. 974 ◽

Cited By ~ 9

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.

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An Automatic Tree Skeleton Extraction Approach Based on Multi-View Slicing Using Terrestrial LiDAR Scans Data

Remote Sensing ◽

10.3390/rs12223824 ◽

2020 ◽

Vol 12 (22) ◽

pp. 3824

Author(s):

Mingyao Ai ◽

Yuan Yao ◽

Qingwu Hu ◽

Yue Wang ◽

Wei Wang

Keyword(s):

Point Cloud ◽

Mean Shift ◽

Principal Component ◽

Point Clouds ◽

Skeleton Extraction ◽

Tree Reconstruction ◽

Terrestrial Lidar ◽

Cloud Data ◽

Critical Technology ◽

X Ray Computed

Effective 3D tree reconstruction based on point clouds from terrestrial Light Detection and Ranging (LiDAR) scans (TLS) has been widely recognized as a critical technology in forestry and ecology modeling. The major advantages of using TLS lie in its rapidly and automatically capturing tree information at millimeter level, providing massive high-density data. In addition, TLS 3D tree reconstruction allows for occlusions and complex structures from the derived point cloud of trees to be obtained. In this paper, an automatic tree skeleton extraction approach based on multi-view slicing is proposed to improve the TLS 3D tree reconstruction, which borrowed the idea from the medical imaging technology of X-ray computed tomography. Firstly, we extracted the precise trunk center and then cut the point cloud of the tree into slices. Next, the skeleton from each slice was generated using the kernel mean shift and principal component analysis algorithms. Accordingly, these isolated skeletons were smoothed and morphologically synthetized. Finally, the validation in point clouds of two trees acquired from multi-view TLS further demonstrated the potential of the proposed framework in efficiently dealing with TLS point cloud data.

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