scholarly journals ПОРІВНЯЛЬНИЙ АНАЛІЗ МЕТОДІВ ПОШУКУ НАЙБЛИЖЧИХ ТОЧОК НА ЗОБРАЖЕННЯХ ОБ’ЄКТІВ ТЕХНІЧНИХ СИСТЕМ

2021 ◽  
pp. 123-130
Author(s):  
М. Д. Мирненко ◽  
Д. М. Крицький ◽  
О. К. Погудіна ◽  
О. С. Крицька
Keyword(s):  
Three Dimensional ◽  
Point Clouds ◽  
Mutual Orientation ◽  
Icp Algorithm ◽  
Three Dimensional Objects ◽  
Key Points ◽  
Template Library ◽  
Cloud Points ◽  
Technical Systems ◽  
Nearest Points

The subject of the study is the process of mapping the construction of point clouds of technical systems using the algorithm of the nearest points. The goal is to minimize the alignment criterion by converting a set of cloud points Y into a set of cloud points X in a software product that uses an iterative closest point (ICP) algorithm. Objectives: to analyze the properties of input images that contain point clouds; to review the algorithms for identifying and comparing key points; implement a cloud comparison algorithm using the ISR algorithm; consider an example of the algorithm for estimating the approximate values of the elements of mutual orientation; implement software that allows you to compare files that contain point clouds and draw conclusions about the possibility of comparing them. The methods used are: search for points using the algorithm of iterative nearest points, the algorithm for estimating the approximate values of the elements of mutual orientation, the method of algorithm theory for the analysis of file structures STL (standard template library - format template library) format. The following results were obtained. The choice of the ICP algorithm for the task of reconstruction of the object of technical systems is substantiated; the main features of the ISR algorithm are considered; the algorithm of comparison of key points, and also optimization that allows reducing criterion of combination at the reconstruction of three-dimensional objects of technical systems results. Conclusions. The study found that the iterative near-point algorithm is more detailed and accurate when modeling objects. At the same time, this method requires very accurate values and when calculating the degree of proximity, the complexity of calculation by this algorithm increases many times. Whereas the algorithm for estimating the approximate values of the elements of mutual orientation does not require information about the approximate orientation of the point clouds, which simplifies the work and reduces the simulation time. It was found that not all files are comparable. Therefore, the software is implemented, which gives an opinion on the possibility of comparing points in the proposed two files, which contain clouds of points in the structure of the STL format.

scholarly journals Métodos de Interpolación Basados en Funciones de Base Radial con Aplicaciones a la Reconstrucción de Imágenes

2018 ◽  
Vol 3 (1) ◽  
pp. 563
Author(s):  
Rodrigo Combe ◽  
Idulfo Arrocha
Keyword(s):  
Surface Reconstruction ◽  
Interpolation Method ◽  
Three Dimensional ◽  
Point Clouds ◽  
Implicit Surface ◽  
Implicit Functions ◽  
Interpolation Methods ◽  
Three Dimensional Objects ◽  
Dimensional Reconstruction ◽  
Base Functions

This article presents the Radial Base Functions (RBFs) as a functional interpolation method for implicit surface reconstruction from points cloud.  These methods allow not only to improve inaccuracies resulting from scanners, but also possible discontinuities that occur in the point clouds.  The complexity of three-dimensional objects makes reconstruction difficult since devices such as scanners do not always faithfully reproduce the objects, which can lead to information gaps or an incomplete reconstruction. Interpolation methods based on RBFs allow to correct these errors.  Three-dimensional surface reconstruction has wide applications in biomedical engineering, in the design of industrial parts, among others.  With the algorithm, we developed we have been able to make reconstructions of both explicit and implicit functions, in two and three dimensions.Keywords:  Radial Basis Functions, Three-dimensional reconstruction, Interpolation Methods.

scholarly journals Modelling and Visualization of Three Dimensional Objects Using Inexpensive Digital Cameras

2018 ◽  
Author(s):  
Ismail Elkhrachy
Keyword(s):  
Low Cost ◽  
Three Dimensional ◽  
Point Clouds ◽  
3D Models ◽  
Bundle Adjustment ◽  
Control Points ◽  
Digital Cameras ◽  
Three Dimensional Objects ◽  
Spatial Positioning ◽  
And Control

This paper analyses and evaluate the precision and the accuracy the capability of low-cost terrestrial photogrammetry by using many digital cameras to construct a 3D model of an object. To obtain the goal, a building façade has imaged by two inexpensive digital cameras such as Canon and Pentax camera. Bundle adjustment and image processing calculated by using Agisoft PhotScan software. Several factors will be included during this study, different cameras, and control points. Many photogrammetric point clouds will be generated. Their accuracy will be compared with some natural control points which collected by the laser total station of the same building. The cloud to cloud distance will be computed for different comparison 3D models to investigate different variables. The practical field experiment showed a spatial positioning reported by the investigated technique was between 2-4cm in the 3D coordinates of a façade. This accuracy is optimistic since the captured images were processed without any control points.

scholarly journals Automated Three-Dimensional Linear Elements Extraction from Mobile LiDAR Point Clouds in Railway Environments

2019 ◽  
Vol 4 (3) ◽  
pp. 46
Author(s):  
Luis Gézero ◽  
Carlos Antunes
Keyword(s):  
Three Dimensional ◽  
Simple Algorithm ◽  
Point Clouds ◽  
Lidar Data ◽  
Quality Information ◽  
Geometric Methods ◽  
Restricted Number ◽  
System Trajectory ◽  
Linear Elements ◽  
Cloud Points

The railway structures need constant monitoring and maintenance to ensure the train circulation safety. Quality information concerning the infrastructure geometry, namely the three-dimensional linear elements, are crucial for that processes. Along with this work, a method to automated extract three-dimensional linear elements from point clouds collected by terrestrial mobile LiDAR systems along railways is presented. The proposed method takes advantage of the stored cloud point’s attributes as an alternative to complex geometric methods applied over the point’s cloud coordinates. Based on the assumption that the linear elements to extract are roughly parallel to the rail tracks and therefore to the system trajectory, the stored scan angle value was used to restrict the number of cloud points that represents the linear elements. A simple algorithm is then applied to that restricted number of points to get the three-dimensional polylines geometry. The obtained values of completeness, correctness and quality, validate the use of the methodology for linear elements extraction from mobile LiDAR data gathered along railway environments.

scholarly journals Transparent Collision Visualization of Point Clouds Acquired by Laser Scanning

2019 ◽  
Vol 8 (9) ◽  
pp. 425
Author(s):  
Weite Li ◽  
Kenya Shigeta ◽  
Kyoko Hasegawa ◽  
Liang Li ◽  
Keiji Yano ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Large Scale ◽  
Three Dimensional ◽  
Point Clouds ◽  
City Government ◽  
Three Dimensional Objects ◽  
Kyoto City ◽  
Risk Areas ◽  
Scale Point ◽  
The City

In this paper, we propose a method to visualize large-scale colliding point clouds by highlighting their collision areas, and apply the method to visualization of collision simulation. Our method uses our recent work that achieved precise three-dimensional see-through imaging, i.e., transparent visualization, of large-scale point clouds that were acquired via laser scanning of three-dimensional objects. We apply the proposed collision visualization method to two applications: (1) The revival of the festival float procession of the Gion Festival, Kyoto city, Japan. The city government plans to revive the original procession route, which is narrow and not used at present. For the revival, it is important to know whether the festival floats would collide with houses, billboards, electric wires, or other objects along the original route. (2) Plant simulations based on laser-scanned datasets of existing and new facilities. The advantageous features of our method are the following: (1) A transparent visualization with a correct depth feel that is helpful to robustly determine the collision areas; (2) the ability to visualize high collision risk areas and real collision areas; and (3) the ability to highlight target visualized areas by increasing the corresponding point densities.

scholarly journals Point Cloud Registration Based on Multiparameter Functional

Mathematics ◽  
2021 ◽  
Vol 9 (20) ◽  
pp. 2589
Author(s):  
Artyom Makovetskii ◽  
Sergei Voronin ◽  
Vitaly Kober ◽  
Aleksei Voronin
Keyword(s):  
Dimensional Space ◽  
Three Dimensional ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Geometric Transformation ◽  
Icp Algorithm ◽  
Registration Algorithm ◽  
Closed Point ◽  
Partial Overlap ◽  
Point To Point

The registration of point clouds in a three-dimensional space is an important task in many areas of computer vision, including robotics and autonomous driving. The purpose of registration is to find a rigid geometric transformation to align two point clouds. The registration problem can be affected by noise and partiality (two point clouds only have a partial overlap). The Iterative Closed Point (ICP) algorithm is a common method for solving the registration problem. Recently, artificial neural networks have begun to be used in the registration of point clouds. The drawback of ICP and other registration algorithms is the possible convergence to a local minimum. Thus, an important characteristic of a registration algorithm is the ability to avoid local minima. In this paper, we propose an ICP-type registration algorithm (λ-ICP) that uses a multiparameter functional (λ-functional). The proposed λ-ICP algorithm generalizes the NICP algorithm (normal ICP). The application of the λ-functional requires a consistent choice of the eigenvectors of the covariance matrix of two point clouds. The paper also proposes an algorithm for choosing the directions of eigenvectors. The performance of the proposed λ-ICP algorithm is compared with that of a standard point-to-point ICP and neural network Deep Closest Points (DCP).

scholarly journals Curvature Adaptive 3D Scanning Transformation Calculation

2018 ◽  
Vol 62 (4) ◽  
pp. 107-116
Author(s):  
Adrián Mezei ◽  
Tibor Kovács
Keyword(s):  
Real Time ◽  
Point Cloud ◽  
Three Dimensional ◽  
Point Clouds ◽  
3D Scanning ◽  
Laser Line ◽  
3D Scanner ◽  
Three Dimensional Objects ◽  
Laser Scanners ◽  
Set Up

Three-dimensional objects can be scanned by 3D laser scanners that use active triangulation. These scanners create three-dimensional point clouds from the scanned objects. The laser line is identified in the images, which are captured at given transformations by the camera, and the point cloud can be calculated from these. The hardest challenge is to construct these transformations so that most of the surface can be captured. The result of a scanning may have missing parts because either not the best transformations were used or because some parts of the object cannot be scanned. Based on the results of the previous scans, a better transformation plan can be created, with which the next scan can be performed. In this paper, a method is proposed for transforming a special 3D scanner into a position from where the scanned point can be seen from an ideal angle. A method is described for estimating this transformation in real-time, so these can be calculated for every point of a previous scan to set up a next improved scan.

scholarly journals Derivation of Three-Dimensional Displacement Vectors from Multi-Temporal Long-Range Terrestrial Laser Scanning at the Reissenschuh Landslide (Tyrol, Austria)

2018 ◽  
Vol 10 (11) ◽  
pp. 1688 ◽  
Author(s):  
Jan Pfeiffer ◽  
Thomas Zieher ◽  
Magnus Bremer ◽  
Volker Wichmann ◽  
Martin Rutzinger
Keyword(s):  
Long Range ◽  
Laser Scanning ◽  
Three Dimensional ◽  
Point Clouds ◽  
Satellite System ◽  
Image Correlation ◽  
Mountain Areas ◽  
Icp Algorithm ◽  
Displacement Vectors ◽  
Multi Temporal

Deep-seated gravitational slope deformations (DSGSDs) endanger settlements and infrastructure in mountain areas all over the world. To prevent disastrous events, their activity needs to be continuously monitored. In this paper, the movement of the Reissenschuh DSGSD in the Schmirn valley (Tyrol, Austria) is quantified based on point clouds acquired with a Riegl VZ®-6000 long-range laser scanner in 2016 and 2017. Geomorphological features (e.g., block edges, terrain ridges, scarps) travelling on top of the landslide are extracted from the acquired point clouds using morphometric attributes based on locally computed eigenvectors and -values. The corresponding representations of the extracted features in the multi-temporal data are exploited to derive 3D displacement vectors based on a workflow exploiting the iterative closest point (ICP) algorithm. The subsequent analysis reveals spatial patterns of landslide movement with mean displacements in the order of 0.62 ma − 1 , corresponding well with measurements at characteristic points using a differential global navigation satellite system (DGNSS). The results are also compared to those derived from a modified version of the well-known image correlation (IMCORR) method using shaded reliefs of the derived digital terrain models. The applied extended ICP algorithm outperforms the raster-based method particularly in areas with predominantly vertical movement.

scholarly journals Estimating intrafraction tumor motion during fiducial-based liver stereotactic radiotherapy via an iterative closest point (ICP) algorithm

2019 ◽  
Vol 14 (1) ◽  
Author(s):  
Wu-zhou Li ◽  
Zhi-wen Liang ◽  
Yi Cao ◽  
Ting-ting Cao ◽  
Hong Quan ◽  
...  
Keyword(s):  
Stereotactic Radiotherapy ◽  
Imaging System ◽  
Liver Tumors ◽  
Three Dimensional ◽  
Rigid Motion ◽  
Iterative Closest Point ◽  
Tumor Motion ◽  
Tracking Accuracy ◽  
Icp Algorithm ◽  
Anterior Posterior

Abstract Background Tumor motion may compromise the accuracy of liver stereotactic radiotherapy. In order to carry out a precise planning, estimating liver tumor motion during radiotherapy has received a lot of attention. Previous approach may have difficult to deal with image data corrupted by noise. The iterative closest point (ICP) algorithm is widely used for estimating the rigid registration of three-dimensional point sets when these data were dense or corrupted. In the light of this, our study estimated the three-dimensional (3D) rigid motion of liver tumors during stereotactic liver radiotherapy using reconstructed 3D coordinates of fiducials based on the ICP algorithm. Methods Four hundred ninety-five pairs of orthogonal kilovoltage (KV) images from the CyberKnife stereo imaging system for 12 patients were used in this study. For each pair of images, the 3D coordinates of fiducial markers inside the liver were calculated via geometric derivations. The 3D coordinates were used to calculate the real-time translational and rotational motion of liver tumors around three axes via an ICP algorithm. The residual error was also investigated both with and without rotational correction. Results The translational shifts of liver tumors in left-right (LR), anterior-posterior (AP),and superior-inferior (SI) directions were 2.92 ± 1.98 mm, 5.54 ± 3.12 mm, and 16.22 ± 5.86 mm, respectively; the rotational angles in left-right (LR), anterior-posterior (AP), and superior-inferior (SI) directions were 3.95° ± 3.08°, 4.93° ± 2.90°, and 4.09° ± 1.99°, respectively. Rotational correction decreased 3D fiducial displacement from 1.19 ± 0.35 mm to 0.65 ± 0.24 mm (P<0.001). Conclusions The maximum translational movement occurred in the SI direction. Rotational correction decreased fiducial displacements and increased tumor tracking accuracy.

scholarly journals Fast Overlap Detection between Hard-Core Colloidal Cuboids and Spheres. The OCSI Algorithm

Algorithms ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 72
Author(s):  
Luca Tonti ◽  
Alessandro Patti
Keyword(s):  
Colloidal Particles ◽  
Dynamic Properties ◽  
Wide Spectrum ◽  
Three Dimensional ◽  
Hard Core ◽  
Detection Algorithm ◽  
Three Dimensional Objects ◽  
Aggregation Behaviour ◽  
Sphere Radius ◽  
User Friendly

Collision between rigid three-dimensional objects is a very common modelling problem in a wide spectrum of scientific disciplines, including Computer Science and Physics. It spans from realistic animation of polyhedral shapes for computer vision to the description of thermodynamic and dynamic properties in simple and complex fluids. For instance, colloidal particles of especially exotic shapes are commonly modelled as hard-core objects, whose collision test is key to correctly determine their phase and aggregation behaviour. In this work, we propose the Oriented Cuboid Sphere Intersection (OCSI) algorithm to detect collisions between prolate or oblate cuboids and spheres. We investigate OCSI’s performance by bench-marking it against a number of algorithms commonly employed in computer graphics and colloidal science: Quick Rejection First (QRI), Quick Rejection Intertwined (QRF) and a vectorized version of the OBB-sphere collision detection algorithm that explicitly uses SIMD Streaming Extension (SSE) intrinsics, here referred to as SSE-intr. We observed that QRI and QRF significantly depend on the specific cuboid anisotropy and sphere radius, while SSE-intr and OCSI maintain their speed independently of the objects’ geometry. While OCSI and SSE-intr, both based on SIMD parallelization, show excellent and very similar performance, the former provides a more accessible coding and user-friendly implementation as it exploits OpenMP directives for automatic vectorization.

scholarly journals OICP: An Online Fast Registration Algorithm Based on Rigid Translation Applied to Wire Arc Additive Manufacturing of Mold Repair

Materials ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1563
Author(s):  
Ruibing Wu ◽  
Ziping Yu ◽  
Donghong Ding ◽  
Qinghua Lu ◽  
Zengxi Pan ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Point Cloud ◽  
Point Clouds ◽  
Iterative Closest Point ◽  
Multiple Point ◽  
Icp Algorithm ◽  
Registration Algorithm ◽  
Cloud Models ◽  
Comparison Results ◽  
Wire Arc Additive Manufacturing

As promising technology with low requirements and high depositing efficiency, Wire Arc Additive Manufacturing (WAAM) can significantly reduce the repair cost and improve the formation quality of molds. To further improve the accuracy of WAAM in repairing molds, the point cloud model that expresses the spatial distribution and surface characteristics of the mold is proposed. Since the mold has a large size, it is necessary to be scanned multiple times, resulting in multiple point cloud models. The point cloud registration, such as the Iterative Closest Point (ICP) algorithm, then plays the role of merging multiple point cloud models to reconstruct a complete data model. However, using the ICP algorithm to merge large point clouds with a low-overlap area is inefficient, time-consuming, and unsatisfactory. Therefore, this paper provides the improved Offset Iterative Closest Point (OICP) algorithm, which is an online fast registration algorithm suitable for intelligent WAAM mold repair technology. The practicality and reliability of the algorithm are illustrated by the comparison results with the standard ICP algorithm and the three-coordinate measuring instrument in the Experimental Setup Section. The results are that the OICP algorithm is feasible for registrations with low overlap rates. For an overlap rate lower than 60% in our experiments, the traditional ICP algorithm failed, while the Root Mean Square (RMS) error reached 0.1 mm, and the rotation error was within 0.5 degrees, indicating the improvement of the proposed OICP algorithm.

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