Interactive Pickup of Three-dimensional Fine Point Cloud Based on GPU

2014 ◽  
Vol 8 (1) ◽  
pp. 631-635
Author(s):  
Ming Huang ◽  
Fang Yang ◽  
Yong Zhang ◽  
Xinle Fu
Keyword(s):  
Point Cloud ◽  
Large Scale ◽  
Cloud Model ◽  
Three Dimensional ◽  
Spatial Transformation ◽  
Three Dimensional Model ◽  
Cloud Data ◽  
Speed Advantage ◽  
Point Cloud Model ◽  
Screen Space

Three-dimensional fine point cloud has gradually become a key data source of three-dimensional model. The large scale point cloud interactive quick pick up is a kind of important operation in the point cloud data processing and applications. Since the point cloud model is composed of massive points, the speed of ordinary picking method is limited. A GPU-based point cloud picking algorithm was thus presented to solve the problem. The basic idea of the algorithm is that by spatial transformation converting the point cloud to screen space, and then, the point was calculated which is the nearest to the mouse click point in screen space. The GPU's parallel computing capabilities were used to achieve spatial transformation and distance comparison by compute shader in this algorithm. So the speed of the pickup has been increased. The results show that compared with the CPU, the pickup method based on GPU has greater speed advantage. Especially for the point cloud over 4 million points, the speed of the pickup has been increased 2-3 times faster.

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 High-Resolution Representation for Mobile Mapping Data in Curved Regular Grid Model

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5373 ◽  
Author(s):  
Jingxin Su ◽  
Ryuji Miyazaki ◽  
Toru Tamaki ◽  
Kazufumi Kaneda
Keyword(s):  
High Resolution ◽  
Point Cloud ◽  
Cloud Model ◽  
Three Dimensional ◽  
Road Surface ◽  
Surface Elevation ◽  
Regular Grid ◽  
Real World Data ◽  
Mobile Mapping ◽  
Cloud Data

As mobile mapping systems become a mature technology, there are many applications for the process of the measured data. One interesting application is the use of driving simulators that can be used to analyze the data of tire vibration or vehicle simulations. In previous research, we presented our proposed method that can create a precise three-dimensional point cloud model of road surface regions and trajectory points. Our data sets were obtained by a vehicle-mounted mobile mapping system (MMS). The collected data were converted into point cloud data and color images. In this paper, we utilize the previous results as input data and present a solution that can generate an elevation grid for building an OpenCRG model. The OpenCRG project was originally developed to describe road surface elevation data, and also defined an open file format. As it can be difficult to generate a regular grid from point cloud directly, the road surface is first divided into straight lines, circular arcs, and and clothoids. Secondly, a non-regular grid which contains the elevation of road surface points is created for each road surface segment. Then, a regular grid is generated by accurately interpolating the elevation values from the non-regular grid. Finally, the curved regular grid (CRG) model files are created based on the above procedures, and can be visualized by OpenCRG tools. The experimental results on real-world data show that the proposed approach provided a very-high-resolution road surface elevation model.

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 Research on the Involvement of Computer Graphics Algorithms in Systems for the Creation of Public Sculpture

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Acheng Zhou ◽  
Chao Gao
Keyword(s):  
Computer Graphics ◽  
Computer Image ◽  
Point Cloud ◽  
Laser Scanning ◽  
Cloud Model ◽  
High Definition ◽  
Cloud Data ◽  
Point Cloud Model ◽  
The Creation ◽  
Public Sculpture

Currently, there is less research on how to improve the efficiency of the application of computer graphics technology in the creation of public sculpture. Therefore, this paper will focus on how computer graphics algorithms can enable systems for the creation of public sculpture with the intervention of computer graphics technology to create more accurate and completed works of public sculpture. It will explore and analyze how computer image algorithms can help creators apply computer image technology to finish complete and accurate public sculptures, and individual studies, computer imagery, and model analysis are also used. In systems for the creation of public sculpture, the point cloud data of the model is obtained through 3D laser scanning technology; then the algorithm of the point cloud model is integrated and the Statistical Outlier Removal algorithm of the point cloud model intervention is processed. By this way, the point cloud model of the work is optimized, and then a more completed and accurate public sculpture work can be produced by 3D sculpting or 3D printing. The research shows that, in the creation of public sculptures with the intervention of computer graphics technology, the computer graphics algorithm acquires the basis of the high-definition public sculpture data model. The computer graphics algorithm improves the accuracy and completeness of the creator using computer graphics technology; it is also the key to transform the accurate enlargement and transformation of the sculptural model into the actual sculptural work.

scholarly journals Large-scale electroholography by HORN-8 from a point-cloud model with 400,000 points

2018 ◽  
Vol 26 (26) ◽  
pp. 34259 ◽  
Author(s):  
Yota Yamamoto ◽  
Hirotaka Nakayama ◽  
Naoki Takada ◽  
Takashi Nishitsuji ◽  
Takashige Sugie ◽  
...  
Keyword(s):  
Point Cloud ◽  
Large Scale ◽  
Cloud Model ◽  
Point Cloud Model

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.

Three-Dimensional Reconstruction of Single Input Image Based on Point Cloud

2021 ◽  
Vol 87 (7) ◽  
pp. 479-484
Author(s):  
Yu Hou ◽  
Ruifeng Zhai ◽  
Xueyan Li ◽  
Junfeng Song ◽  
Xuehan Ma ◽  
...  
Keyword(s):  
Point Cloud ◽  
Large Scale ◽  
Three Dimensional ◽  
Data Sets ◽  
Point Cloud Data ◽  
Three Dimensional Reconstruction ◽  
Data Set ◽  
Cloud Data ◽  
Dimensional Reconstruction ◽  
Institute Of Technology

Three-dimensional reconstruction from a single image has excellent future prospects. The use of neural networks for three-dimensional reconstruction has achieved remarkable results. Most of the current point-cloud-based three-dimensional reconstruction networks are trained using nonreal data sets and do not have good generalizability. Based on the Karlsruhe Institute of Technology and Toyota Technological Institute at Chicago ()data set of large-scale scenes, this article proposes a method for processing real data sets. The data set produced in this work can better train our network model and realize point cloud reconstruction based on a single picture of the real world. Finally, the constructed point cloud data correspond well to the corresponding three-dimensional shapes, and to a certain extent, the disadvantage of the uneven distribution of the point cloud data obtained by light detection and ranging scanning is overcome using the proposed method.

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