Three-dimensional Data Points Research Articles

CLUSTERIZATION METHOD BASED ON BREADTH FIRST SEARCH OR BFS FOR A GRAPH

Clusterization is one of the types of algorithms of unsupervised learning. The idea behind it is that an algorithm learns patterns from untagged data. Such type of algorithm helps to find unseen dependencies in the untagged data itself. This paper presented algorithms based on Breadth-First Search or BFS for a Graph. The method was built based on the basic theory of clusterization. To the theory of clusterization, the calculated distance between the two farthest points in the cluster should be less than the distance between the closest two points from different clusters. By this rule, we defined that two parameters of the method should be the maximum distance between points by which these can be connected and assumed to be in one cluster. The second had to be the maximum distance in the cluster, aka the cluster’s diameter. A cluster’s diameter is the farthest distance between two points within a cluster. With these hyperparameters and the defined distance method, we can assume that every point is a vertex of a graph, two points within the threshold of the distance between pairs of ones are neighbours, and count the connection between counts as an edge of a graph. The group of connected vertexes or a particular vertex is a graph. The diameter hyperparameter ought to keep the data homogeneity in a cluster. We can define every graph as a cluster with defined rules based on previous assumptions. Later in this paper will be visualized the clusterization of three-dimensional data points. We took one of the most popular clusterization dataset – the iris dataset for visualizing purposes. The paper contains several examples of clusterization of the dataset with different hyperparameters. We took KMeans [3] as an example of the clusterization method. The method based on BFS is a flexible clusterization method that relies on meta-information about distancing between data points.

Improve three-dimensional point localization accuracy in stereo vision systems using a novel camera calibration method

Computer vision systems have demonstrated to be useful in applications of autonomous navigation, especially with the use of stereo vision systems for the three-dimensional mapping of the environment. This article presents a novel camera calibration method to improve the accuracy of stereo vision systems for three-dimensional point localization. The proposed camera calibration method uses the least square method to model the error caused by the image digitalization and the lens distortion. To obtain particular three-dimensional point coordinates, the stereo vision systems use the information of two images taken by two different cameras. Then, the system locates the two-dimensional pixel coordinates of the three-dimensional point in both images and coverts them into angles. With the obtained angles, the system finds the three-dimensional point coordinates through a triangulation process. The proposed camera calibration method is applied in the stereo vision systems, and a comparative analysis between the real and calibrated three-dimensional data points is performed to validate the improvements. Moreover, the developed method is compared with three classical calibration methods to analyze their advantages in terms of accuracy with respect to tested methods.

Relationships between Satellite-Based Spectral Burned Ratios and Terrestrial Laser Scanning

Three-dimensional point data acquired by Terrestrial Lidar Scanning (TLS) is used as ground observation in comparisons with fire severity indices computed from Landsat satellite multi-temporal images through Google Earth Engine (GEE). Forest fires are measured by the extent and severity of fire. Current methods of assessing fire severity are limited to on-site visual inspection or the use of satellite and aerial images to quantify severity over larger areas. On the ground, assessment of fire severity is influenced by the observers’ knowledge of the local ecosystem and ability to accurately assess several forest structure measurements. The objective of this study is to introduce TLS to validate spectral burned ratios obtained from Landsat images. The spectral change was obtained by an image compositing technique through GEE. The 32 plots were collected using TLS in Wood Buffalo National Park, Canada. TLS-generated 3D points were converted to voxels and the counted voxels were compared in four height strata. There was a negative linear relationship between spectral indices and counted voxels in the height strata between 1 to 5 m to produce R2 value of 0.45 and 0.47 for unburned plots and a non-linear relationship in the height strata between 0 to 0.5m for burned plots to produce R2 value of 0.56 and 0.59. Shrub or stand development was related with the spectral indices at unburned plots, and vegetation recovery in the ground surface was related at burned plots. As TLS systems become more cost efficient and portable, techniques used in this study will be useful to produce objective assessments of structure measurements for fire refugia and ecological response after a fire. TLS is especially useful for the quick ground assessments which are needed for forest fire applications.

基于特征信息分类的三维点数据去噪

基于特征信息分类的三维点数据去噪

Erosion Analysis for Archaeological Purposes using Lidar

The purpose of this paper is to report the findings of an investigation (using GID techniques) into whether erosion in the uplands of North Yorshire, England, poses an immediate threat to the exisiting archaeology in the area. Lidar, a remote sensing technique for light detection and ranging to determine three-dimensional data points, is applied to the database. As a result, water runoff and increased user pressure have been noted as the two main dangers to the landscape. Furthermore, this paper concludes that the primary erosion factor that affects upland archaeology is related to human activity, which can be documented using Lidar technology.

Detection of High-Speed Railway Subsidence and Geometry Irregularity Using Terrestrial Laser Scanning

Subsidence and geometry deformation monitoring are essential for safe transportation on a high-speed railway. Terrestrial laser scanning (TLS) is able to collect dense three-dimensional point data from the survey scene and achieve highly accurate measurements; therefore, it is considered to be one of the most promising surveying techniques for railway track geometry deformation monitoring. This paper proposes a new approach that uses TLS to detect subsidence and irregularities in a track by fitting boundaries of the cross section of the track. In addition, for a section of local railway, an outdoor experiment was performed to ascertain the feasibility and accuracy of this method. The deformations detected with TLS were compared with the field measurements gathered with other methods such as those from a track inspection car. The results indicate that the subsidence difference between TLS and precise leveling is 2–3 mm, and the difference in the geometric parameters of the tracks is 1–2 mm. Finally, the possible causes of error involved with TLS are discussed.

Kinect Depth Data Segmentation Based on Gauss Mixture Model Clustering

Indoor scene understanding based on the depth image data is a cutting-edge issue in the field of three-dimensional computer vision. Taking the layout characteristics of the indoor scenes and more plane features in these scenes into account, this paper presents a depth image segmentation method based on Gauss Mixture Model clustering. First, transform the Kinect depth image data into point cloud which is in the form of discrete three-dimensional point data, and denoise and down-sample the point cloud data; second, calculate the point normal of all points in the entire point cloud, then cluster the entire normal using Gaussian Mixture Model, and finally implement the entire point clouds segmentation by RANSAC algorithm. Experimental results show that the divided regions have obvious boundaries and segmentation quality is above normal, and lay a good foundation for object recognition.

항공 라이다 데이터로부터 확장 카이 알고리즘을 이용한 건물경계선 추출

It is essential and fundamental to extract boundary information of target object via massive three-dimensional point data acquired from laser scanner. Especially extracting boundary information of manmade features such as buildings is quite important because building is one of the major components consisting complex contemporary urban area, and has artificially defined shape. In this research, extended -algorithm using geometry information of point data was proposed to extract boundary information of building from three-dimensional point data consisting building. The proposed algorithm begins with composing Delaunay triangulation process for given points and removes edges satisfying specific conditions process. Additionally, to make whole boundary extraction process efficient, we used Sweep-hull algorithm for constructing Delaunay triangulation. To verify the performance of the proposed extended -algorithm, we compared the proposed algorithm with Encasing Polygon Generating Algorithm and -Shape Algorithm, which had been researched in the area of feature extraction. Further, the extracted boundary information from the proposed algorithm was analysed against manually digitized building boundary in order to test accuracy of the result of extracting boundary. The experimental results showed that extended -algorithm proposed in this research proved to improve the speed of extracting boundary information compared to the existing algorithm with a higher accuracy for detecting boundary information.

Accelerating Fourier volume rendering by polar coordinate data representation

Volume rendering is an important tool to visualize three-dimensional data in biomedicine by projecting the data to a two-dimensional plane. The projection is done by ray casting and its complexity is proportional to the number of three-dimensional data points. To reduce complexity, Fourier volume rendering (FVR) uses slice projection theorem to facilitate the integration of voxels along the ray casting path. In this paper, we proposed a new method for FVR that stored and processed the frequency domain data in polar coordinate. By exploiting three aspects of data processing which is previously impossible in rectilinear coordinate, our new method is much faster than the previous methods. The first aspect is data regularity. When data are stored in polar coordinate, extracting a slice involves accessing data stored in adjacent memory location. This regularity makes memory access more efficient. The second aspect is to utilize the high data density near the origin in polar coordinate. We can obtain two benefits from this aspect. The first allows us to extract a slice by nearest-neighbor interpolation instead of more complex interpolation but without sacrificing image quality. The second allows us to trade off between image quality and memory storage. The third aspect is to recognize that converting from rectilinear coordinate to polar coordinate is a one-time process. Therefore, we can use a better interpolation kernel with larger support in coordinate conversion. In turn, most of the computation is shifted to the preprocessing stage and interactive rendering can be made very fast. In the experiments, we show that the speed in interactive visualization for our new method is independent of the size of the interpolation kernel, therefore, achieving comparable image quality at a faster rate than previous methods.

From points to numbers: a database-driven approach to convert terrestrial LiDAR point clouds to tree volumes

Data provided by airborne LiDAR (Light Detection and Ranging) techniques are commonly used for measuring and monitoring of forest resources. In the last years, terrestrial laser scanning systems have also gained increasing attention as a ground-based application for individual tree measurements. The database-driven approach described in this article focuses on the calculation of single tree stem volumes from terrestrial laser scan data. A straightforward way of determining the volume of upright cylindrical objects is presented. Three-dimensional point data are split up into cylindrical sections whose diameters are used as basis for volume calculations. Tests conducted with a simple tree stem model show that results produced by the algorithm differ by less than 1 % from results obtained by manual section-wise cubing, which was applied to gather reference data. The method we present offers a perspective on non-destructive techniques of volume data acquisition for forest resource assessments. It is capable of reducing the error of single tree biomass estimations which in turn would improve the reliability of biomass inventories. Also, other tasks such as forest inventories or forest management plans can benefit from more accurate estimations and smaller prediction errors.

Research on Parametric Modeling Technology of Marine Propeller

Marine propeller is a kind of typical part with free-form surface. The surface shape of propeller blade is very complex, and it is difficult to be described by general mathematical expressions, so that the modeling of propeller is very cumbersome and error-prone. To solve this problem, firstly, three-dimensional point data of propeller blade is calculated by VC/MFC programming technology and access technology of database based on projection principle of propeller blade. And then, automation and parameterization of solid modeling for propeller of different map forms are realized by programming with the interactive programming language of UG/Open Grip based on UG software. The parametric modeling system provides accurate three-dimensional model for automatic programming, dynamic simulation and numerical analysis of propeller, and improves the design efficiency of systematic propeller, and has very good practical value.

A global optimization procedure for the location of a median line in the three-dimensional space

A global optimization procedure is proposed to find a line in the Euclidean three-dimensional space which minimizes the sum of distances to a given finite set of three-dimensional data points. Although we are using similar techniques as for location problems in two dimensions, it is shown that the problem becomes much harder to solve. However, a problem parameterization as well as lower bounds are suggested whereby we succeeded in solving medium-size instances in a reasonable amount of computing time.

Fast Three-Dimensional Discrete Cosine Transform

A fast three-dimensional discrete cosine transform algorithm (3D FCT) and a fast 3D inverse cosine transform (3D IFCT) algorithm are presented, suitable for analysis of 3D data points. Many existing algorithms for three-dimensional data points make use of either the 1D cosine transform or both the 2D and 1D cosine transforms. Existing algorithms based on the 1D discrete cosine transform (DCT) apply the separable 1D transform to the data points in the x, y, and z directions, respectively, while those based on 2D and 1D transforms apply the 2D cosine transform for the x-y planes and then the 1D cosine transform in the z direction. The proposed 3D DCT algorithms handle the 3D data points directly and have been shown to be computationally efficient. They involve a 3D decomposition process where a data volume is recursively decomposed in each dimension until unit data cubes are obtained. The algorithms are presented in the form of a signal flow graph which captures the various computations involved. A complexity analysis along with empirical results is included, demonstrating the performance of the proposed direct 3D DCT algorithms. As 3D FCT and IFCT are symmetric and relatively fast, they can be used in any application requiring a real-time symmetric codec, such as video conferencing, online multiparty video games, and three-dimensional graphics rendering.

Lossless compression of 3-D point data in QSplat representation

We propose a lossless compression algorithm for three-dimensional point data in graphics applications. In typical point representation, each point is treated as a sphere and its geometrical and normal data are stored in the hierarchical structure of bounding spheres. The proposed algorithm sorts child spheres according to their positions to achieve a higher coding gain for geometrical data. Also, the proposed algorithm compactly encodes normal data by exploiting high correlation between parent and child normals. Simulation results show that the proposed algorithm saves up to 60% of storage space.

Surface design using triangular patches

Surfaces designed in a computer graphics environment have many applications, including the design of cars, airplanes, shipbodies and modeling robots. The generation of smooth surfaces from a set of three-dimensional data points is a key problem in the field of Computer Aided Geometric Design. An overview of fundamental triangular concepts is given and new results are presented.

Surface fitting for interactive shape design

A method is presented to create smooth B- spline surfaces on basis of three-dimensional data points. The intended application is interactive shape design and shape representation with CAD workstations. This type of application allows specific assumptions to be made on the input data points, and it implies requirements in terms of speed and integration of the method with at least a graphics environment. There are no restrictions on the data locations, they need not fit into a grid and may be irregularly spaced. In an automatic process, the data are first represented by an array of B- spline curves. The parametrizations of these are harmonized as to enable approximate representation (within some tolerance) by a single B- spline surface with the smallest possible number of control points. The fitting system can also be used to represent a large amount of data points by an approximating surface, thus providing data compression.

Automatic smoothing with geometric surface patches

The generation of smooth surfaces from a mesh of three-dimensional data points is an important problem in the field of Computer Aided Geometric Design. A new smoothing method based upon generalized Coons' patches and calculus of variation is presented.

Increasing the smoothness of bicubic spline surfaces

The generation of smooth and shape-preserving surfaces from a mesh of three-dimensional data points is a very important problem in the field of Computer Aided Geometric Design. If a mesh of curves has been obtained in a first interpolation step and they fulfil the user requirements, the surface generation algorithm must avoid further oscillations within the patches. However, it is known that unwanted oscillations may appear using C 2 surfaces. In the present paper, a smoothing algorithm for bicubic spline surfaces is presented. Having the piecewise cubic boundaries of the patches fixed, the algorithm chooses adequate twists factors in order to increase the smoothness. At the end, although the surface is only C 1, unwanted oscillations have been reduced. Practical examples and test results are presented and discussed.