scholarly journals Point Distribution as True Quality of LiDAR Point Cloud

2017 ◽  
Vol 5 (4) ◽  
Author(s):  
Sergejs Kodors
Keyword(s):  
Point Cloud ◽  
Point Distribution

scholarly journals Quantification Method for the Uncertainty of Matching Point Distribution on 3D Reconstruction

2020 ◽  
Vol 9 (4) ◽  
pp. 187
Author(s):  
Yuxia Bian ◽  
Xuejun Liu ◽  
Meizhen Wang ◽  
Hongji Liu ◽  
Shuhong Fang ◽  
...  
Keyword(s):  
3D Reconstruction ◽  
Point Cloud ◽  
Point Distribution ◽  
Multiple Images ◽  
Quantification Method ◽  
Real Scene ◽  
Direct Data ◽  
The Relationship ◽  
Overlapping Region

Matching points are the direct data sources of the fundamental matrix, camera parameters, and point cloud calculation. Thus, their uncertainty has a direct influence on the quality of image-based 3D reconstruction and is dependent on the number, accuracy, and distribution of the matching points. This study mainly focuses on the uncertainty of matching point distribution. First, horizontal dilution of precision (HDOP) is used to quantify the feature point distribution in the overlapping region of multiple images. Then, the quantization method is constructed. H D O P ∗ ¯ , the average of 2 × arctan ( H D O P × n 5 − 1 ) / π on all images, is utilized to measure the uncertainty of matching point distribution on 3D reconstruction. Finally, simulated and real scene experiments were performed to describe and verify the rationality of the proposed method. We found that the relationship between H D O P ∗ ¯ and the matching point distribution in this study was consistent with that between matching point distribution and 3D reconstruction. Consequently, it may be a feasible method to predict the quality of 3D reconstruction by calculating the uncertainty of matching point distribution.

scholarly journals Test Charts for Evaluating Imaging and Point Cloud Quality of Mobile Mapping Systems for Urban Street Space Acquisition

2021 ◽  
Vol 13 (2) ◽  
pp. 237
Author(s):  
Norbert Pfeifer ◽  
Johannes Falkner ◽  
Andreas Bayr ◽  
Lothar Eysn ◽  
Camillo Ressl
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Practical Method ◽  
Image Resolution ◽  
Test Method ◽  
Mobile Mapping ◽  
Point Distribution ◽  
Mobile Mapping Systems ◽  
Mapping Systems

Mobile mapping is in the process of becoming a routinely applied standard tool to support administration of cities. For ensuring the usability of the mobile mapping data it is necessary to have a practical method to evaluate the quality of different systems, which reaches beyond 3D accuracy of individual points. Such a method must be objective, easy to implement, and provide quantitative results to be used in tendering processes. We present such an approach which extracts quality figures for point density, point distribution, point cloud planarity, image resolution, and street sign legibility. In its practical application for the mobile mapping campaign of the City of Vienna (Austria) in 2020 the proposed test method proved to fulfill the above requirements. As an additional result, quality figures are reported for the panorama images and point clouds of three different mobile mapping systems.

scholarly journals THE POWER LINE HEIGHT ANALYSIS CHART DRAWING BASE ON MAPMATRIX

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 327-333
Author(s):  
Y. R. He ◽  
W. W. Ma ◽  
X. R. Wang ◽  
J. Q. Dai ◽  
J. L. Zheng
Keyword(s):  
Point Cloud ◽  
Remote Sensing Data ◽  
Field Investigation ◽  
Power Line ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Binocular Stereo Vision ◽  
Binocular Stereo ◽  
Aerial Vehicle

Abstract. The power patrol has been completed by manual field investigation, which is inefficient, costly and unsafe. In order to extract the height of the power line and its surrounding ground objects more quickly and conveniently, and better service for power line patrol. This paper uses remote sensing data of unmanned aerial vehicle to carry out aerial triangulation, stereo model establishment and binocular stereo vision height extraction base on MapMatrix software, then obtains the power line height analysis chart. Then LiDAR point cloud data is used to verify the accuracy of the power line height analysis chart. The results show that this method not only meets the standard of power line patrol, but also improves the efficiency and quality of power line patrol.

scholarly journals ASSESSMENT OF THE QUALITY OF DIGITAL TERRAIN MODEL PRODUCED FROM UNMANNED AERIAL SYSTEM IMAGERY

2016 ◽  
Vol XLI-B1 ◽  
pp. 893-899
Author(s):  
M. Kosmatin Fras ◽  
A. Kerin ◽  
M. Mesarič ◽  
V. Peterman ◽  
D. Grigillo
Keyword(s):  
Quality Assessment ◽  
Point Cloud ◽  
Digital Terrain Model ◽  
Quality Parameter ◽  
Unmanned Aerial System ◽  
Test Field ◽  
Absolute Deviation ◽  
Terrain Model ◽  
Digital Terrain

Production of digital terrain model (DTM) is one of the most usual tasks when processing photogrammetric point cloud generated from Unmanned Aerial System (UAS) imagery. The quality of the DTM produced in this way depends on different factors: the quality of imagery, image orientation and camera calibration, point cloud filtering, interpolation methods etc. However, the assessment of the real quality of DTM is very important for its further use and applications. In this paper we first describe the main steps of UAS imagery acquisition and processing based on practical test field survey and data. The main focus of this paper is to present the approach to DTM quality assessment and to give a practical example on the test field data. For data processing and DTM quality assessment presented in this paper mainly the in-house developed computer programs have been used. The quality of DTM comprises its accuracy, density, and completeness. Different accuracy measures like RMSE, median, normalized median absolute deviation and their confidence interval, quantiles are computed. The completeness of the DTM is very often overlooked quality parameter, but when DTM is produced from the point cloud this should not be neglected as some areas might be very sparsely covered by points. The original density is presented with density plot or map. The completeness is presented by the map of point density and the map of distances between grid points and terrain points. The results in the test area show great potential of the DTM produced from UAS imagery, in the sense of detailed representation of the terrain as well as good height accuracy.

A New Kinect-Based Scanning System and its Application

2015 ◽  
Vol 764-765 ◽  
pp. 1375-1379 ◽  
Author(s):  
Cheng Tiao Hsieh
Keyword(s):  
Point Cloud ◽  
Depth Map ◽  
Bilateral Filter ◽  
Point Clouds ◽  
Digital Fabrication ◽  
Surface Model ◽  
Scanning System ◽  
3D Registration ◽  
Noise Point

This paper aims at presenting a simple approach utilizing a Kinect-based scanner to create models available for 3D printing or other digital manufacturing machines. The outputs of Kinect-based scanners are a depth map and they usually need complicated computational processes to prepare them ready for a digital fabrication. The necessary processes include noise filtering, point cloud alignment and surface reconstruction. Each process may require several functions and algorithms to accomplish these specific tasks. For instance, the Iterative Closest Point (ICP) is frequently used in a 3D registration and the bilateral filter is often used in a noise point filtering process. This paper attempts to develop a simple Kinect-based scanner and its specific modeling approach without involving the above complicated processes.The developed scanner consists of an ASUS’s Xtion Pro and rotation table. A set of organized point cloud can be generated by the scanner. Those organized point clouds can be aligned precisely by a simple transformation matrix instead of the ICP. The surface quality of raw point clouds captured by Kinect are usually rough. For this drawback, this paper introduces a solution to obtain a smooth surface model. Inaddition, those processes have been efficiently developed by free open libraries, VTK, Point Cloud Library and OpenNI.

scholarly journals body2vec: 3D Point Cloud Reconstruction for Precise Anthropometry with Handheld Devices

2020 ◽  
Vol 6 (9) ◽  
pp. 94
Author(s):  
Magda Alexandra Trujillo-Jiménez ◽  
Pablo Navarro ◽  
Bruno Pazos ◽  
Leonardo Morales ◽  
Virginia Ramallo ◽  
...  
Keyword(s):  
Network Architecture ◽  
Point Cloud ◽  
Point Clouds ◽  
Noise Removal ◽  
Extraction Methods ◽  
Anthropometric Measurements ◽  
Handheld Devices ◽  
Background Removal ◽  
3D Point Cloud Reconstruction

Current point cloud extraction methods based on photogrammetry generate large amounts of spurious detections that hamper useful 3D mesh reconstructions or, even worse, the possibility of adequate measurements. Moreover, noise removal methods for point clouds are complex, slow and incapable to cope with semantic noise. In this work, we present body2vec, a model-based body segmentation tool that uses a specifically trained Neural Network architecture. Body2vec is capable to perform human body point cloud reconstruction from videos taken on hand-held devices (smartphones or tablets), achieving high quality anthropometric measurements. The main contribution of the proposed workflow is to perform a background removal step, thus avoiding the spurious points generation that is usual in photogrammetric reconstruction. A group of 60 persons were taped with a smartphone, and the corresponding point clouds were obtained automatically with standard photogrammetric methods. We used as a 3D silver standard the clean meshes obtained at the same time with LiDAR sensors post-processed and noise-filtered by expert anthropological biologists. Finally, we used as gold standard anthropometric measurements of the waist and hip of the same people, taken by expert anthropometrists. Applying our method to the raw videos significantly enhanced the quality of the results of the point cloud as compared with the LiDAR-based mesh, and of the anthropometric measurements as compared with the actual hip and waist perimeter measured by the anthropometrists. In both contexts, the resulting quality of body2vec is equivalent to the LiDAR reconstruction.

The Research of Point Cloud Data Processing Technology

2014 ◽  
Vol 628 ◽  
pp. 426-431
Author(s):  
Li Bo Zhou ◽  
Fu Lin Xu ◽  
Su Hua Liu
Keyword(s):  
Data Processing ◽  
Point Cloud ◽  
Quality Data ◽  
Surface Generation ◽  
Point Cloud Data ◽  
Data Smoothing ◽  
Cloud Data ◽  
Data Point ◽  
Cloud Data Processing

Data processing is a key to reverse engineering, the results of which will directly affect the quality of the model reconstruction. Eliminate noise points are the first step in data processing, The method of using Coons surface to determine the noise in the data point is proposed. To reduce the amount of calculation and improve the surface generation efficiency, data point is reduced. According to the surrounding point coordinate information, the defect coordinates are interpolated. Data smoothing can improve the surface generation quality, data block can simplify the creation of the surface. Auto parts point cloud data is processed, and achieve the desired effect.

scholarly journals Construction and application of factory digital twin model based on BIM and point cloud

2021 ◽  
Vol 293 ◽  
pp. 02031
Author(s):  
Guocheng Qin ◽  
Ling Wang ◽  
YiMei Hou ◽  
HaoRan Gui ◽  
YingHao Jian
Keyword(s):  
Local Structure ◽  
Point Cloud ◽  
Point Clouds ◽  
Digital Factory ◽  
Digital Twin ◽  
Twin Model ◽  
Model Adjustment ◽  
Solid Foundation ◽  
Actual Location

The digital twin model of the factory is the basis for the construction of a digital factory, and the professional system of the factory is complex. The traditional BIM model is not completely consistent with the actual position of the corresponding component, and it is difficult to directly replace the digital twin model. In response to this situation, relying on a certain factory project, the point cloud is used to eliminate the positional deviation between the BIM model and the factory during the construction phase, improve the efficiency and accuracy and reliability of model adjustment and optimization, and , realize the conversion from BIM model to digital twin model. A novel algorithm is developed to quickly detect and evaluate the construction quality of the local structure of the factory, so as to input the initial deformation data of the structure into the corresponding model and feed back to the construction party for improvement. The results show that the digital twin model, which is highly consistent with the actual location of the factory components, not only lays a solid foundation for the construction of a digital factory, but also further deepens the integration and application of BIM and point clouds.

scholarly journals Subsoil Recognition for Road Investment Supported by the Integration of Geodetic and GPR Data in the Form of a Point Cloud

2021 ◽  
Vol 13 (19) ◽  
pp. 3886 ◽  
Author(s):  
Łukasz Ortyl ◽  
Marta Gabryś
Keyword(s):  
Ground Penetrating Radar ◽  
Point Cloud ◽  
Geophysical Methods ◽  
Road Construction ◽  
Spatial Visualization ◽  
Design Stage ◽  
Quality Of Results ◽  
Structure Surface ◽  
Ground Penetrating

During road construction investments, the key issue affecting the structure’s safety is accurate subsoil recognition. Identifying subsoil variability zones or natural voids can be performed using geophysical methods, and ground-penetrating radar (GPR) is recommended for this task as it identifies the location and spatial range karst formations. This paper describes the methodology of acquisition and processing of GPR data for ground recognition for road investment. Additional subsoil research was performed after karst phenomena were identified in the investment area, formations not revealed by geological recognition from earlier studies during the pre-design stage. Mala Ramac CU II radar with a 250 MHz antenna and a Leica DS2000 with 250 and 700 MHz antennas with real-time geopositioning were used to obtain the data. Regarding GPR data postprocessing, we present a method of converting spatial visualization into a point cloud that allows for GPR and geodetic data integration and confrontation. This approach enabled us to determine the locations of control trenches, the results of which were used for material validation, which is necessary to improve the reliability of subsoil recognition. The results showed a high correlation between the recorded GPR signals and the subsoil structure. Additionally, differences in the quality of results for measurements conducted before laying supporting layers with slag and on the completed road structure surface are illustrated.

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