Examination of Efficiency of Bridge Periodic Inspection Using 3D Data (Point Cloud Data and Images)

2020 ◽  
Author(s):  
Tatsuru Ninomiya ◽  
Mami Enomoto ◽  
Mitsuharu Shimokawa ◽  
Tatsuya Hattori ◽  
Yasushi Nitta
Keyword(s):  
Point Cloud ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Periodic Inspection ◽  
3D Data ◽  
Data Point

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.

A Novel Pre-Processing Approach of Point Cloud in Reverse Engineering

2013 ◽  
Vol 331 ◽  
pp. 631-635
Author(s):  
Ci Zhang ◽  
Guo Fan Hu ◽  
Xu Bing Chen
Keyword(s):  
Reverse Engineering ◽  
Point Cloud ◽  
Triangular Grid ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Engineering Data ◽  
Processing Approach ◽  
3D Data ◽  
Cloud Data Processing

In reverse engineering, data pre-processing has played an increasingly important role for rebuilding the original 3D model. However, it is usually complex, time-consuming, and difficult to realize, as there are huge amounts of redundant 3D data existed in the gained point cloud. To find a solution for this issue, point cloud data processing and streamlining technologies are reviewed firstly. Secondly, a novel pre-processing approach is proposed in three steps: point cloud registration, regional 3D triangular mesh construction and point cloud filtering. And then, the projected hexagonal area and the closest projected point are defined. At last, a parabolic antenna model is employed as a case study. After pre-processing, the number of points are decreased from 4,066,282 to 449,806 under the constraint of triangular grid size h equaling to 2mm, i.e. about 1/9 size of the original point cloud. The result demonstrates its feasibility and efficiency.

scholarly journals A Lightweight, Robust Exploitation System for Temporal Stacks of UAS Data: Use Case for Forward-Deployed Military or Emergency Responders

Drones ◽  
2019 ◽  
Vol 3 (1) ◽  
pp. 29 ◽  
Author(s):  
Andrew Marx ◽  
Yu-Hsi Chou ◽  
Kevin Mercy ◽  
Richard Windisch
Keyword(s):  
Urban Areas ◽  
Point Cloud ◽  
Point Clouds ◽  
Temporal Analysis ◽  
Unmanned Aerial Systems ◽  
Point Cloud Data ◽  
High Quality ◽  
Cloud Data ◽  
3D Data ◽  
Exploitation System

The availability and precision of unmanned aerial systems (UAS) permit the repeated collection of very-high quality three-dimensional (3D) data to monitor high-interest areas, such as dams, urban areas, or erosion-prone coastlines. However, challenges exist in the temporal analysis of this data, specifically in conducting change-detection analysis on the high-quality point cloud data. These files are very large in size and contain points in varying locations that do not align between scenes. These large file sizes also limit the use of this data for individuals with low computational resources, such as first responders or forward-deployed soldiers. In response, this manuscript presents an approach that aggregates data spatially into voxels to provide the user with a lightweight, web-based exploitation system coupled with a flexible backend database. The system creates a robust set of tools to analyze large temporal stacks of 3D data and reduces data size by 78%, all while being able to query the original point cloud data. This approach offers a solution for organizations analyzing high-resolution, temporal point-clouds, as well as a possible solution for operations in areas with poor computational and connectivity resources requiring high-quality, 3D data for decision support and planning.

A CAD Modeling Method of Dental Surface Based on Scanning Data

2012 ◽  
Vol 151 ◽  
pp. 111-115
Author(s):  
Li Cheng Fan ◽  
Feng Feng Zhang
Keyword(s):  
Point Cloud ◽  
Cnc Machining ◽  
Tooth Surface ◽  
Iteration Algorithm ◽  
Point Cloud Data ◽  
Scanning Method ◽  
Cad Modeling ◽  
Cloud Data ◽  
Data Registration ◽  
3D Data

The measurement of the teeth surface and the CAD modeling of the point cloud data are the key basics for the following CNC machining, and the complete data can only be obtained through multi-perspective scanning method. Using ICP iteration algorithm that based on point-to-line to the multi-perspective scanning data, specific to the features of layering scanning, retrograde the 3D data registration to 2D planar registration, and provide the cutting and splicing algorithm for registered tooth data, obtain precise and integrated tooth surface point cloud data, which serves as the CAD model for the following CNC machining.

scholarly journals MODELLING PERMAFROST TERRAIN USING KINEMATIC, DUAL-WAVELENGTH LASER SCANNING

2020 ◽  
Vol V-2-2020 ◽  
pp. 749-756
Author(s):  
A. Kukko ◽  
H. Kaartinen ◽  
G. Osinski ◽  
J. Hyyppä
Keyword(s):  
Standard Deviation ◽  
Data Processing ◽  
Point Cloud ◽  
Laser Scanning ◽  
Base Station ◽  
Dual Wavelength ◽  
The Arctic ◽  
Point Cloud Data ◽  
Cloud Data ◽  
3D Data

Abstract. In this paper we introduce the first dual-wavelength, kinematic backpack laser scanning system and its application on high resolution 3D terrain modelling of permafrost landforms. We discuss the data processing pipeline from acquisition to preparation, system calibration and terrain model process. Topographic information is vital for planning and monitoring tasks in urban planning, road construction for mass calculations, and mitigation of flood and wind related risks by structural design in coastal areas. 3D data gives possibility to understand natural processes inducing changes in the terrain, such as the cycles of thaw-freeze in permafrost regions. Through an application case on permafrost landforms in the Arctic we present the field practices and data processing applied, characterize the data output and discuss the precision and accuracy of the base station, trajectory and point cloud data. Two pulsed time of flight ranging, high performance mobile laser scanners were used in combination with a near navigation grade GNSS-IMU positioning on a kinematic backpack platform. The study shows that with a high-end system 15 mm absolute accuracy of 3D data could be achieved using PPP processing for the GNSS base station and multi-pass differential trajectory post-processing. The PPP solution shows millimetre level agreement (Easting 6 mm, Northing 4 mm, and elevation 8 mm standard deviations) for the base station coordinates over an 11 day period. The point cloud residual standard deviation for angular boresight misalignment was 27 mm. The absolute distance between ground surfaces from interactive analysis was 17 mm with 13 mm standard deviation (n = 64). The proposed backpack laser scanning provides accurate and precise 3D data and performance over considerable land surface area for detailed elevation modelling and analysis of the morphology of features of interest. The high density point cloud data permits fusion of the dual-wavelength lidar reflectance data into spectral products.

scholarly journals Ekstraksi Digital Surface Model (DSM) dari Data Unmanned Aerial Vehicle (UAV) Berbasis Point Cloud

2020 ◽  
Vol 3 (2) ◽  
Author(s):  
Indra Laksana ◽  
R Suharyadi ◽  
M. Pramono Hadi
Keyword(s):  
Data Acquisition ◽  
Point Cloud ◽  
Unmanned Aircraft ◽  
Surface Model ◽  
Digital Surface Model ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Key Points ◽  
Elevation Data ◽  
Data Point

<div class="WordSection1"><p><strong>Abstr</strong><strong>ak. </strong>Akuisisi data dengan menggunakan pesawat tanpa awak semakin sering dilakukan. Penelitian ini memodelkan data elevasi dari pengukuran lapangan dengan menggunakan pesawat tanpa awak. Tujuan dari penelitian ini :(1) untuk menguji kemampuan pesawat tanpa awak dalam mengakuisisi data elevasi, dan (2) untuk membandingkan data elevasi jika ditambahkan data point cloud dan data pengukuran batimetri. Metode pengolahan dengan menggunakan data point cloud dilakukan dengan pertama-tama mencocokkan titik kunci. Pencocokan titik kunci mengkaitkan seluruh hasil foto udara hingga membentuk satu kesatuan area yang telah difoto. Selanjutnya dilakukan penampalan titik ikat pada area yang telah terbentuk dari pencocokan titik kunci. Titik ikat berfungsi sebagai koreksi data pada saat pesawat tanpa awak melakukan pengambilan data. Foto udara yang telah dikoreksi kemudian diolah untuk mendapatkan data <em>point cloud</em>. <em>Point cloud</em> berguna sebagai data penyusun ortofoto dan data <em>Digital Surface Model</em> (DSM). Pengolahan data point cloud hingga menghasilkan DSM dilakukan dengan menggunakan software Pix4D dan Agisoft photoscan. Hasil yang diperoleh menunjukkan bahwa terjadi peningkatan kemampuan DSM ketika data pointcloud ditambahkan data titik ikat dan data pengukuran batimetri. Sehingga dapat disimpulkan bahwa akuisisi data menggunakan pesawat tanpa awak mampu menghasilkan data yang dapat dipercaya. Selain dapat dipercaya akuisisi data dengan pesawat tanpa awak lebih murah jika dibandingkan dengan akuisisi data dengan foto udara.</p><p>Keywords:  digital surface model, pesawat tanpa awak, titik ikat</p><p><strong> </strong></p><p><strong>Abstract. </strong><em>Data acquisition using unmanned aircraft is increasingly being done. This study models elevation data from field measurements using unmanned aircraft. The purpose of this study: (1) to test the ability of unmanned aircraft to acquire elevation data, and (2) to compare elevation data if added point cloud data and bathymetry measurement data. The processing method using point cloud data is done by first matching key points. Matching key points links all aerial photography results to forming a single unit area that has been photographed.</em><em> </em><em>Next, a tie point is carried out in the area formed from matching key points. Tie points function as data correction when unmanned aircraft take data. Corrected aerial photos are then processed to obtain point cloud data.</em><em> </em><em>Point cloud is useful as orthophoto compiler data and Digital Surface Model (DSM) data.</em><em> </em><em>Point cloud data processing to produce DSM is done using Pix4D and Agisoft photoscan software.</em><em>The results obtained showed that there was an increase in DSM capabilities when point cloud data was added to the tie point data and bathymetry measurement data. So, it can be concluded that data acquisition using unmanned aircraft is able to produce reliable data. Besides being reliable, data acquisition with unmanned aircraft is cheaper compared to data acquisition with aerial photography.</em></p></div><strong><em>Keywords:</em> </strong>u<em>nmanned aerial vehicle, ground c point, Digital surface model</em><p class="MsoNormal" style="margin-bottom: .0001pt; text-align: justify; text-justify: inter-ideograph;"> </p>

Surface Feature Extraction Based on Curvelet Transform from Point Cloud Data

2015 ◽  
Vol 719-720 ◽  
pp. 1236-1243
Author(s):  
Hong Juan Yang ◽  
Ji Wen Chen ◽  
Yun Chu Zhang ◽  
Xiao Dong Xu
Keyword(s):  
Feature Extraction ◽  
Point Cloud ◽  
Nonlinear Function ◽  
Curvelet Transform ◽  
Surface Feature ◽  
Initial Surface ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Transform Coefficients ◽  
Data Point

Aiming at multi directions analysis problem of surface feature extraction from point cloud data, Curvelet transform is introduced to multi directions analysis of point cloud data. Based on the preprocessing of location and expansion, second-generation discrete Curvelet transform is used to analyze point cloud data. Curvelet transform coefficients are processed to enhance the contour of point cloud data. Nonlinear function is used to process Curvelet transform coefficients of coarse layer. Compromise for soft and hard thresholds is used to process Curvelet transform coefficients of detail layer. Piecewise nonlinear function is used to process Curvelet transform coefficients of fine layer. The data point is reconstructed from the enhanced Curvelet transform coefficient with Curvelet inverse transformation. Initial surface feature is achieved with edge detection. The precise surface feature is achieved with morphological dilation and erosion to filter edge without real shape significance. Example of part point cloud data of brake shell shows the proposed surface feature extraction method can accurately extract surface feature from data point.

Automatic Detection of Tangential Discontinuities in Point Cloud Data

2008 ◽  
Vol 8 (2) ◽  
Author(s):  
Hao Song ◽  
Hsi-Yung Feng ◽  
Daoshan OuYang
Keyword(s):  
Geometric Modeling ◽  
Point Cloud ◽  
Shape Change ◽  
Surface Shape ◽  
Underlying Surface ◽  
Point Cloud Data ◽  
Data Set ◽  
Cloud Data ◽  
Tangential Discontinuities ◽  
Data Point

A point cloud data set, a dense set of discrete coordinate points scanned or sampled from the surface of a 3D physical object or design model, is emerging as a new representation format for geometric modeling. This paper presents a new method to detect tangential discontinuities in point cloud data. The method introduces an original criterion, named as incompatibility, to quantify the magnitude of shape change in the vicinity of a data point. The introduced criterion is unique since in smooth regions of the underlying surface where shape change around a data point is small, the calculated incompatibilities tend to cluster around small values. At points close to tangential discontinuities, the calculated incompatibilities become relatively large. By modeling the incompatibilities of points in smooth regions following a statistical distribution, the proposed method identifies tangential discontinuities as those points whose incompatibilities are considered outliers with respect to the distribution. As the categorization of outliers is in effect independent of the underlying surface shape and sampling conditions of the data points, a threshold can be automatically determined via a generic procedure and used to identify tangential discontinuities. The effectiveness of the proposed method is demonstrated through many case studies using both simulated and practical point cloud data sets.

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