scholarly journals VISUALIZATION OF POINT CLOUD MODELS IN MOBILE AUGMENTED REALITY USING CONTINUOUS LEVEL OF DETAIL METHOD

2020 ◽  
Vol XLIV-4/W1-2020 ◽  
pp. 167-170
Author(s):  
L. Zhang ◽  
P. van Oosterom ◽  
H. Liu
Keyword(s):  
Augmented Reality ◽  
Point Cloud ◽  
Cloud Model ◽  
Point Clouds ◽  
Level Of Detail ◽  
Mobile Augmented Reality ◽  
Cloud Models ◽  
Point Cloud Model ◽  
Visual Artifacts ◽  
Mobile Ar

Abstract. Point clouds have become one of the most popular sources of data in geospatial fields due to their availability and flexibility. However, because of the large amount of data and the limited resources of mobile devices, the use of point clouds in mobile Augmented Reality applications is still quite limited. Many current mobile AR applications of point clouds lack fluent interactions with users. In our paper, a cLoD (continuous level-of-detail) method is introduced to filter the number of points to be rendered considerably, together with an adaptive point size rendering strategy, thus improve the rendering performance and remove visual artifacts of mobile AR point cloud applications. Our method uses a cLoD model that has an ideal distribution over LoDs, with which can remove unnecessary points without sudden changes in density as present in the commonly used discrete level-of-detail approaches. Besides, camera position, orientation and distance from the camera to point cloud model is taken into consideration as well. With our method, good interactive visualization of point clouds can be realized in the mobile AR environment, with both nice visual quality and proper resource consumption.

scholarly journals Hole Repairing Algorithm for 3D Point Cloud Model of Symmetrical Objects Grasped by the Manipulator

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7558
Author(s):  
Linyan Cui ◽  
Guolong Zhang ◽  
Jinshen Wang
Keyword(s):  
Point Cloud ◽  
Cloud Model ◽  
Engineering Application ◽  
Point Clouds ◽  
Real Point ◽  
3D Point Cloud ◽  
Virtual Camera ◽  
3D Point Clouds ◽  
Cloud Models ◽  
Point Cloud Model

For the engineering application of manipulator grasping objects, mechanical arm occlusion and limited imaging angle produce various holes in the reconstructed 3D point clouds of objects. Acquiring a complete point cloud model of the grasped object plays a very important role in the subsequent task planning of the manipulator. This paper proposes a method with which to automatically detect and repair the holes in the 3D point cloud model of symmetrical objects grasped by the manipulator. With the established virtual camera coordinate system and boundary detection, repair and classification of holes, the closed boundaries for the nested holes were detected and classified into two kinds, which correspond to the mechanical claw holes caused by mechanical arm occlusion and the missing surface produced by limited imaging angle. These two kinds of holes were repaired based on surface reconstruction and object symmetry. Experiments on simulated and real point cloud models demonstrate that our approach outperforms the other state-of-the-art 3D point cloud hole repair algorithms.

scholarly journals Primitive Shape Fitting in Point Clouds Using the Bees Algorithm

2019 ◽  
Vol 9 (23) ◽  
pp. 5198 ◽  
Author(s):  
Luca Baronti ◽  
Mark Alston ◽  
Nikos Mavrakis ◽  
Amir M. Ghalamzan E. ◽  
Marco Castellani
Keyword(s):  
Point Cloud ◽  
Ad Hoc ◽  
Cloud Model ◽  
Point Clouds ◽  
Bees Algorithm ◽  
Shape Fitting ◽  
Cloud Models ◽  
Point Cloud Model ◽  
Random Sample Consensus ◽  
Fitting In

In this study the problem of fitting shape primitives to point cloud scenes was tackled as a parameter optimisation procedure, and solved using the popular bees algorithm. Tested on three sets of clean and differently blurred point cloud models, the bees algorithm obtained performances comparable to those obtained using the state-of-the-art random sample consensus (RANSAC) method, and superior to those obtained by an evolutionary algorithm. Shape fitting times were compatible with real-time application. The main advantage of the bees algorithm over standard methods is that it doesn’t rely on ad hoc assumptions about the nature of the point cloud model like RANSAC approximation tolerance.

scholarly journals Comparison of SfM computer vision point clouds of a landslide derived from multiple small UAV platforms and sensors to a TLS-based model

2016 ◽  
Vol 4 (4) ◽  
pp. 246-265 ◽  
Author(s):  
Samantha Ruggles ◽  
Joseph Clark ◽  
Kevin W. Franke ◽  
Derek Wolfe ◽  
Brandon Reimschiissel ◽  
...  
Keyword(s):  
Computer Vision ◽  
Point Cloud ◽  
Cloud Model ◽  
Point Clouds ◽  
Image Resolution ◽  
Three Dimensions ◽  
Cloud Models ◽  
Small Uav ◽  
Point Cloud Model ◽  
Selection Of

Structure from motion (SfM) computer vision is a remote sensing method that is gaining popularity due to its simplicity and ability to accurately characterize site geometry in three dimensions (3D). While many researchers have demonstrated the potential for SfM to be used with unmanned aerial vehicles (UAVs) to model in 3D various geologic features, such as landslides, little is understood concerning how the selection of the UAV platform can affect the resolution and accuracy of the model. This study evaluates the resolution and accuracy of 3D point cloud models of a large landslide that occurred in 2013 near Page, Arizona, that were developed from various small UAV platform and camera configurations. Terrestrial laser scans were performed at the landslide and were used to establish a comparative baseline model. Results from the study indicate that point cloud resolution improved by more than 16% when using multi-rotor UAVs instead of fixed-wing UAVs. However, accuracy of the points in the point cloud model appear to be independent of the UAV platform, but depend principally on the selected camera and the image resolution. Additional practical guidance on flying various UAV platforms in challenging field conditions is provided for geologists and engineers.

scholarly journals MULTI-SENSOR 3D RECORDING PIPELINE FOR THE DOCUMENTATION OF JAVANESE TEMPLES

2019 ◽  
Vol XLII-2/W15 ◽  
pp. 829-834
Author(s):  
A. Murtiyos ◽  
P. Grussenmeyer ◽  
D. Suwardhi ◽  
W. A. Fadilah ◽  
H. A. Permana ◽  
...  
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Cloud Model ◽  
Point Clouds ◽  
Heritage Sites ◽  
Advantages And Disadvantages ◽  
3D Point Clouds ◽  
Point Cloud Model ◽  
Geometric Quality ◽  
Different Characteristics

<p><strong>Abstract.</strong> 3D recording is an important procedure in the conservation of heritage sites. This past decade, a myriad of 3D sensors has appeared in the market with different advantages and disadvantages. Most notably, the laser scanning and photogrammetry methods have become some of the most used techniques in 3D recording. The integration of these different sensors is an interesting topic, one which will be discussed in this paper. Integration is an activity to combine two or more data with different characteristics to produce a 3D model with the best results. The discussion in this study includes the process of acquisition, processing, and analysis of the geometric quality from the results of the 3D recording process; starting with the acquisition method, registration and georeferencing process, up to the integration of laser scanning and photogrammetry 3D point clouds. The final result of the integration of the two point clouds is the 3D point cloud model that has become a single entity. Some detailed parts of the object of interest draw both geometric and textural information from photogrammetry, while laser scanning provided a point cloud depicting the overall overview of the building. The object used as our case study is Sari Temple, located in Special Region of Yogyakarta, Indonesia.</p>

scholarly journals Dust Removal from 3D Point Cloud Data in Mine Plane Areas Based on Orthogonal Total Least Squares Fitting and GA-TELM

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Jingli Wang ◽  
Huiyuan Zhang ◽  
Jingxiang Gao ◽  
Dong Xiao
Keyword(s):  
Least Squares ◽  
Point Cloud ◽  
Cloud Model ◽  
Hollow Structure ◽  
Total Least Squares ◽  
3D Point Cloud ◽  
Denoising Method ◽  
Least Squares Fitting ◽  
Cloud Models ◽  
Point Cloud Model

With the further development of the construction of “smart mine,” the establishment of three-dimensional (3D) point cloud models of mines has become very common. However, the truck operation caused the 3D point cloud model of the mining area to contain dust points, and the 3D point cloud model established by the Context Capture modeling software is a hollow structure. The previous point cloud denoising algorithms caused holes in the model. In view of the above problems, this paper proposes the point cloud denoising method based on orthogonal total least squares fitting and two-layer extreme learning machine improved by genetic algorithm (GA-TELM). The steps are to separate dust points and ground points by orthogonal total least squares fitting and use GA-TELM to repair holes. The advantages of the proposed method are listed as follows. First, this method could denoise without generating holes, which solves engineering problems. Second, GA-TELM has a better effect in repairing holes compared with the other methods considered in this paper. Finally, this method starts from actual problems and could be used in mining areas with the same problems. Experimental results demonstrate that it can remove dust spots in the flat area of the mine effectively and ensure the integrity of the model.

Discrete Stress Analysis on Point-Cloud Model Derived From Medical Images

2009 ◽  
Author(s):  
Jia Lu ◽  
Jing Qian
Keyword(s):  
Finite Element ◽  
Point Cloud ◽  
Solid Mechanics ◽  
Medical Images ◽  
Cloud Model ◽  
Point Clouds ◽  
Mechanical Analysis ◽  
Patient Specific ◽  
Element Mesh ◽  
Point Cloud Model

Pixel or voxel data from the medical images provide a point-cloud depiction for complicated anatomies that are difficult to describe in CAD geometry. Traditionally, a point-cloud model needs to be converted into finite element mesh in order to perform mechanical analysis. Although meshing generation tools have been significantly improved over the last decades, generating high quality meshes in complicated bodies remains a challenge. Recently, the authors developed a family of solid mechanics solvers that work directly on domains represented by point-clouds [1,2]. Using this method, it is possible to conduct mechanical analysis on point-cloud representations of patient-specific organs without resorting to finite element method. In this article, we describe this paradigm of analysis and demonstrate the method with numerical examples.

scholarly journals A novel watermarking algorithm for three-dimensional point-cloud models based on vertex curvature

2019 ◽  
Vol 15 (1) ◽  
pp. 155014771982604 ◽  
Author(s):  
Jing Liu ◽  
Yajie Yang ◽  
Douli Ma ◽  
Wenjuan He ◽  
Yinghui Wang
Keyword(s):  
Root Mean Square ◽  
Point Cloud ◽  
Cloud Model ◽  
Three Dimensional ◽  
Mean Square ◽  
Geometric Attack ◽  
Cloud Models ◽  
Watermark Embedding ◽  
Point Cloud Model ◽  
Watermarking Scheme

A new blind watermarking scheme for three-dimensional point-cloud models is proposed based on vertex curvature to achieve an appropriate trade-off between transparency and robustness. The root mean square curvature of local set of every vertex is first calculated for the three-dimensional point-cloud model and then the vertices with larger root mean square curvature are used to carry the watermarking information; the vertices with smaller root mean square curvature are exploited to establish the synchronization relation between the watermark embedding and extraction. The three-dimensional point-cloud model is divided into ball rings, and the watermarking information is inserted by modifying the radial radii of vertices within ball rings. Those vertices taking part in establishing the synchronization relation do not carry the watermarking information; therefore, the synchronization relation is not affected by the embedded watermark. Experimental results show the proposed method outperforms other well-known three-dimensional point-cloud model watermarking methods in terms of imperceptibility and robustness, especially for against geometric attack.

scholarly journals Optimum Assembly Planning for Modular Construction Using BIM and 3D Point Clouds

2016 ◽  
Author(s):  
Mohammad Nahangi ◽  
Christopher Rausch ◽  
Carl Haas
Keyword(s):  
Point Cloud ◽  
Cloud Model ◽  
Point Clouds ◽  
Assembly Planning ◽  
Modular Construction ◽  
Optimization Framework ◽  
3D Point Clouds ◽  
Information Models ◽  
Building Information ◽  
Point Cloud Model

Geometric and dimensional deviations often create challenges for component aggregation in the assembly of interchangeable components in modular construction. Although the components are designed interchangeably, once they are fabricated, there are inevitable discrepancies between the designed and built states. Such discrepancies create problems for fitting interchangeable modular components. This paper presents a framework for optimally planning the assembly of interchangeable components based on their as-built state. A 3D point cloud model is captured and the critical interfaces between modules are compared to the original state, integrated in the building information models (BIM), as 3D drawings. The optimization framework is implemented based on two different approaches: (1) minimization of the total deviation for minimizing rework, and (2) intervention of rework by finding the best matching component for each investigated slot. Results show that the method can be effectively used for reducing rework in modular construction by optimum assembly planning.

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