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 GEOREFERENCING AND REPROJECTION ERROR INVESTIGATION ON IMAGE BASED 3D DIGITIZATION AND MAPPING OF HISTORICAL BUILDINGS

2019 ◽  
Vol XLII-2/W11 ◽  
pp. 71-75
Author(s):  
C. Altuntas
Keyword(s):  
Point Cloud ◽  
Large Scale ◽  
Cloud Model ◽  
Low Cost ◽  
Three Dimensional ◽  
Control Points ◽  
Historical Buildings ◽  
Reprojection Error ◽  
Dense Point ◽  
Point Cloud Model

<p><strong>Abstract.</strong> Image based dense point cloud creation is easy and low-cost application for three dimensional digitization of small and large scale objects and surfaces. It is especially attractive method for cultural heritage documentation. Reprojection error on conjugate keypoints indicates accuracy of the model and keypoint localisation in this method. In addition, sequential registration of the images from large scale historical buildings creates big cumulative registration error. Thus, accuracy of the model should be increased with the control points or loop close imaging. The registration of point point cloud model into the georeference system is performed using control points. In this study historical Sultan Selim Mosque that was built in sixteen century by Great Architect Sinan was modelled via photogrammetric dense point cloud. The reprojection error and number of keypoints were evaluated for different base/length ratio. In addition, georeferencing accuracy was evaluated with many configuration of control points with loop and without loop closure imaging.</p>

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 Measurement Method Based on Multispectral Three-Dimensional Imaging for the Chlorophyll Contents of Greenhouse Tomato Plants

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3345 ◽  
Author(s):  
Guoxiang Sun ◽  
Xiaochan Wang ◽  
Ye Sun ◽  
Yongqian Ding ◽  
Wei Lu
Keyword(s):  
Point Cloud ◽  
Cloud Model ◽  
Three Dimensional ◽  
Measurement Model ◽  
View Point ◽  
Nondestructive Measurement ◽  
Tomato Plants ◽  
Single View ◽  
Greenhouse Tomato ◽  
Point Cloud Model

Nondestructive plant growth measurement is essential for researching plant growth and health. A nondestructive measurement system to retrieve plant information includes the measurement of morphological and physiological information, but most systems use two independent measurement systems for the two types of characteristics. In this study, a highly integrated, multispectral, three-dimensional (3D) nondestructive measurement system for greenhouse tomato plants was designed. The system used a Kinect sensor, an SOC710 hyperspectral imager, an electric rotary table, and other components. A heterogeneous sensing image registration technique based on the Fourier transform was proposed, which was used to register the SOC710 multispectral reflectance in the Kinect depth image coordinate system. Furthermore, a 3D multiview RGB-D image-reconstruction method based on the pose estimation and self-calibration of the Kinect sensor was developed to reconstruct a multispectral 3D point cloud model of the tomato plant. An experiment was conducted to measure plant canopy chlorophyll and the relative chlorophyll content was measured by the soil and plant analyzer development (SPAD) measurement model based on a 3D multispectral point cloud model and a single-view point cloud model and its performance was compared and analyzed. The results revealed that the measurement model established by using the characteristic variables from the multiview point cloud model was superior to the one established using the variables from the single-view point cloud model. Therefore, the multispectral 3D reconstruction approach is able to reconstruct the plant multispectral 3D point cloud model, which optimizes the traditional two-dimensional image-based SPAD measurement method and can obtain a precise and efficient high-throughput measurement of plant chlorophyll.

Three Dimensional Modeling of Buildings Using Three Dimensional Laser Scanning Data

2012 ◽  
Vol 594-597 ◽  
pp. 2398-2401
Author(s):  
Dong Ling Ma ◽  
Jian Cui ◽  
Fei Cai
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
3D Visualization ◽  
Cloud Model ◽  
Three Dimensional ◽  
3D Models ◽  
Three Dimensional Modeling ◽  
Dimensional Modeling ◽  
Feature Lines ◽  
Point Cloud Model

This paper provides a scheme to construct three dimensional (3D) model fast using laser scanning data. In the approach, firstly, laser point cloud are scanned from different scan positions and the point cloud coming from neighbor scan stations are spliced automatically to combine a uniform point cloud model, and then feature lines are extracted through the point cloud, and the framework of the building are extracted to generate 3D models. At last, a conclusion can be drawn that 3D visualization model can be generated quickly using 3D laser scanning technology. The experiment result shows that it will bring the application model and technical advantage which traditional mapping way can not have.

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.

Fast Computer-Generated Hologram Generation Method for Three-Dimensional Point Cloud Model

2016 ◽  
Vol 12 (12) ◽  
pp. 1688-1694 ◽  
Author(s):  
Ping Su ◽  
Wenbo Cao ◽  
Jianshe Ma ◽  
Bingchao Cheng ◽  
Xianting Liang ◽  
...  
Keyword(s):  
Point Cloud ◽  
Cloud Model ◽  
Three Dimensional ◽  
Computer Generated Hologram ◽  
Fast Computer ◽  
Point Cloud Model

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.

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