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 Trajectory Planning for Spray Painting Robot Based on Point Cloud Slicing Technique

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 908 ◽  
Author(s):  
Wei Chen ◽  
Xu Li ◽  
Huilin Ge ◽  
Lei Wang ◽  
Yuhang Zhang
Keyword(s):  
Trajectory Planning ◽  
Point Cloud ◽  
Laser Scanning ◽  
Cloud Model ◽  
Normal Vector ◽  
Point Cloud Data ◽  
Spray Painting ◽  
Cloud Data ◽  
Painting Robot ◽  
Point Cloud Model

In this paper, aiming at the problem of poor quality and low spraying efficiency of irregular for complex freeform surfaces, a new spray painting robot trajectory planning method based on point cloud slicing technology is proposed. Firstly, the point cloud data of the workpiece to be sprayed is obtained by laser scanning. The point cloud data is processed to obtain the point cloud model of the sprayed workpiece. Then the section polysemy line is obtained after slice acquisition and section data processing of the point cloud model. The section polysemy line is sampled on average, and the normal vector of all sampling points is estimated. Finally, interpolation algorithm is used to connect the data points to obtain the space trajectory of spraying robot. In addition, the droplet trajectory model for electrostatic spray painting is established. The experimental results show that the method fully meets the requirements of coating thickness and improves the spraying efficiency and uniformity of coating.

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 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 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.

scholarly journals THREE-DIMENSIONAL RECORDING OF BASTION MIDDLEBURG MONUMENT USING TERRESTRIAL LASER SCANNER

2016 ◽  
Vol XLI-B5 ◽  
pp. 323-326
Author(s):  
Z. Majid ◽  
C. L. Lau ◽  
A. R. Yusoff
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Cloud Model ◽  
Three Dimensional ◽  
Third Party ◽  
Scanning System ◽  
Point Cloud Data ◽  
Data Set ◽  
Cloud Data ◽  
Spherical Target

This paper describes the use of terrestrial laser scanning for the full three-dimensional (3D) recording of historical monument, known as the Bastion Middleburg. The monument is located in Melaka, Malaysia, and was built by the Dutch in 1660. This monument serves as a major hub for the community when conducting commercial activities in estuaries Malacca and the Dutch build this monument as a control tower or fortress. The monument is located on the banks of the Malacca River was built between Stadhuys or better known as the Red House and Mill Quayside. The breakthrough fort on 25 November 2006 was a result of the National Heritage Department through in-depth research on the old map. The recording process begins with the placement of measuring targets at strategic locations around the monument. Spherical target was used in the point cloud data registration. The scanning process is carried out using a laser scanning system known as a terrestrial scanner Leica C10. This monument was scanned at seven scanning stations located surrounding the monument with medium scanning resolution mode. Images of the monument have also been captured using a digital camera that is setup in the scanner. For the purposes of proper registration process, the entire spherical target was scanned separately using a high scanning resolution mode. The point cloud data was pre-processed using Leica Cyclone software. The pre-processing process starting with the registration of seven scan data set through overlapping spherical targets. The post-process involved in the generation of coloured point cloud model of the monument using third-party software. The orthophoto of the monument was also produced. This research shows that the method of laser scanning provides an excellent solution for recording historical monuments with true scale of and texture.

scholarly journals Development of Improved Semi-Automated Processing Algorithms for the Creation of Rockfall Databases

2021 ◽  
Vol 13 (8) ◽  
pp. 1479
Author(s):  
Heather Schovanec ◽  
Gabriel Walton ◽  
Ryan Kromer ◽  
Adam Malsam
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Rock Slope ◽  
Terrestrial Laser Scanning ◽  
Multiscale Model ◽  
Current Data ◽  
Cloud Data ◽  
Cloud Processing ◽  
Point Cloud Processing ◽  
The Creation

While terrestrial laser scanning and photogrammetry provide high quality point cloud data that can be used for rock slope monitoring, their increased use has overwhelmed current data analysis methodologies. Accordingly, point cloud processing workflows have previously been developed to automate many processes, including point cloud alignment, generation of change maps and clustering. However, for more specialized rock slope analyses (e.g., generating a rockfall database), the creation of more specialized processing routines and algorithms is necessary. More specialized algorithms include the reconstruction of rockfall volumes from clusters and points and automatic classification of those volumes are both processing steps required to automate the generation of a rockfall database. We propose a workflow that can automate all steps of the point cloud processing workflow. In this study, we detail adaptions to commonly used algorithms for rockfall monitoring use cases, such as Multiscale Model to Model Cloud Comparison (M3C2). This workflow details the entire processing pipeline for rockfall database generation using terrestrial laser scanning.

scholarly journals THREE-DIMENSIONAL RECORDING OF BASTION MIDDLEBURG MONUMENT USING TERRESTRIAL LASER SCANNER

2016 ◽  
Vol XLI-B5 ◽  
pp. 323-326
Author(s):  
Z. Majid ◽  
C. L. Lau ◽  
A. R. Yusoff
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Cloud Model ◽  
Three Dimensional ◽  
Third Party ◽  
Scanning System ◽  
Point Cloud Data ◽  
Data Set ◽  
Cloud Data ◽  
Spherical Target

This paper describes the use of terrestrial laser scanning for the full three-dimensional (3D) recording of historical monument, known as the Bastion Middleburg. The monument is located in Melaka, Malaysia, and was built by the Dutch in 1660. This monument serves as a major hub for the community when conducting commercial activities in estuaries Malacca and the Dutch build this monument as a control tower or fortress. The monument is located on the banks of the Malacca River was built between Stadhuys or better known as the Red House and Mill Quayside. The breakthrough fort on 25 November 2006 was a result of the National Heritage Department through in-depth research on the old map. The recording process begins with the placement of measuring targets at strategic locations around the monument. Spherical target was used in the point cloud data registration. The scanning process is carried out using a laser scanning system known as a terrestrial scanner Leica C10. This monument was scanned at seven scanning stations located surrounding the monument with medium scanning resolution mode. Images of the monument have also been captured using a digital camera that is setup in the scanner. For the purposes of proper registration process, the entire spherical target was scanned separately using a high scanning resolution mode. The point cloud data was pre-processed using Leica Cyclone software. The pre-processing process starting with the registration of seven scan data set through overlapping spherical targets. The post-process involved in the generation of coloured point cloud model of the monument using third-party software. The orthophoto of the monument was also produced. This research shows that the method of laser scanning provides an excellent solution for recording historical monuments with true scale of and texture.

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.

An Efficient and Accurate Method of Relief Segmentation

2011 ◽  
Vol 464 ◽  
pp. 596-599
Author(s):  
Bo Xiang ◽  
Lu Ling An ◽  
Jin Hu Sun ◽  
Lai Shui Zhou
Keyword(s):  
Point Cloud ◽  
Cloud Model ◽  
Accurate Method ◽  
Initial Contour ◽  
Point Cloud Data ◽  
Segmentation Method ◽  
Grid Data ◽  
Cloud Data ◽  
Point Cloud Model ◽  
Map Grids

Authors create a relief segmentation method on point cloud model, and solve such problems as how to store the point cloud data, how to obtain the final contour, how to define Snakes energy term and how to acquire region from its contour. Firstly, the point cloud data is resampled by applying Z-MAP grid data structure. Then initial contour is drawn by interaction, and the total energy of the contour is computed to optimize the contour to the energy-minimizing position by iterations. Finally, the contour is scattered as points, and the points are mapped to Z-MAP grids for projection points. According to these projection points, the region is obtained.

scholarly journals Linear-Based Incremental Co-Registration of MLS and Photogrammetric Point Clouds

2021 ◽  
Vol 13 (11) ◽  
pp. 2195
Author(s):  
Shiming Li ◽  
Xuming Ge ◽  
Shengfu Li ◽  
Bo Xu ◽  
Zhendong Wang
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Registration Method ◽  
Cloud Data ◽  
Linear Feature ◽  
Data Registration ◽  
Source Point ◽  
Urban Remote Sensing ◽  
Single Data

Today, mobile laser scanning and oblique photogrammetry are two standard urban remote sensing acquisition methods, and the cross-source point-cloud data obtained using these methods have significant differences and complementarity. Accurate co-registration can make up for the limitations of a single data source, but many existing registration methods face critical challenges. Therefore, in this paper, we propose a systematic incremental registration method that can successfully register MLS and photogrammetric point clouds in the presence of a large number of missing data, large variations in point density, and scale differences. The robustness of this method is due to its elimination of noise in the extracted linear features and its 2D incremental registration strategy. There are three main contributions of our work: (1) the development of an end-to-end automatic cross-source point-cloud registration method; (2) a way to effectively extract the linear feature and restore the scale; and (3) an incremental registration strategy that simplifies the complex registration process. The experimental results show that this method can successfully achieve cross-source data registration, while other methods have difficulty obtaining satisfactory registration results efficiently. Moreover, this method can be extended to more point-cloud sources.

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