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.

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 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 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.

Opening State Estimation of Mouth Using 3D Point Cloud Model of Nose

2019 ◽  
Vol 2019 (0) ◽  
pp. 2A1-S09
Author(s):  
Takuo SUZUKI ◽  
Kazuki ITO ◽  
Yasushi NAKAUCHI ◽  
Shigeo TANABE ◽  
Masahiko MUKAINO
Keyword(s):  
State Estimation ◽  
Point Cloud ◽  
Cloud Model ◽  
3D Point Cloud ◽  
Point Cloud Model

scholarly journals 3d point cloud model of human bio form created by the application of geometric morphometrics and method of anatomical features: human tibia example

Filomat ◽  
2019 ◽  
Vol 33 (4) ◽  
pp. 1217-1225
Author(s):  
Nikola Vitkovic ◽  
Ljiljana Radovic ◽  
Miroslav Trajanovic ◽  
Miodrag Manic
Keyword(s):  
Geometric Morphometrics ◽  
Point Cloud ◽  
Cloud Model ◽  
Anatomical Landmarks ◽  
3D Point Cloud ◽  
Specific Patient ◽  
Human Tibia ◽  
Biological Form ◽  
Point Cloud Model ◽  
The Creation

Morphometrics refers to the quantitative analysis of a biological form and it can be used to describe its shape. Common types of geometric morphometrics are Landmark-based Geometric Morphometrics which describe shape by using anatomical landmarks (e.g. points), and Outline-based geometric morphometrics which uses envelope curves to describe shape of the biological form (e.g. bone), and they are not absolutely exclusive. Geometric morphometrics can be used for the creation of statistical models which represent shape variation of specific bio form. In this paper, novel application of geometric morphometrics for the creation of personalized models of unique bio-forms, i.e. models which are created for the specific patient is presented. Personalized model is defined as 3D point cloud model of biological form (in this case human tibia). Positions of points in 3D space are determined by using set of parametric functions defined by applying geometrical morphometrics, morphology properties and statistical analysis on the input set of human tibia samples. By using this technique, anatomically correct and geometrically accurate personalized models of bio forms can be created and used in pre, intra, and post-operative procedures in clinical practice.

Sign in / Sign up

Export Citation Format

Share Document