scholarly journals VISUALIZATION OF THE CONSTRUCTION OF ANCIENT ROMAN BUILDINGS IN OSTIA USING POINT CLOUD DATA

2017 ◽  
Vol XLII-2/W3 ◽  
pp. 345-352
Author(s):  
Y. Hori ◽  
T. Ogawa
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Feature Matching ◽  
Three Dimensional ◽  
Point Clouds ◽  
Final Report ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Coverage Accuracy ◽  
New Research

The implementation of laser scanning in the field of archaeology provides us with an entirely new dimension in research and surveying. It allows us to digitally recreate individual objects, or entire cities, using millions of three-dimensional points grouped together in what is referred to as "point clouds". In addition, the visualization of the point cloud data, which can be used in the final report by archaeologists and architects, should usually be produced as a JPG or TIFF file. Not only the visualization of point cloud data, but also re-examination of older data and new survey of the construction of Roman building applying remote-sensing technology for precise and detailed measurements afford new information that may lead to revising drawings of ancient buildings which had been adduced as evidence without any consideration of a degree of accuracy, and finally can provide new research of ancient buildings. We used laser scanners at fields because of its speed, comprehensive coverage, accuracy and flexibility of data manipulation. Therefore, we “skipped” many of post-processing and focused on the images created from the meta-data simply aligned using a tool which extended automatic feature-matching algorithm and a popular renderer that can provide graphic results.

The Advantages of Using Laser Scanners in Surveying in Protected Sites

2015 ◽  
pp. 382-402
Author(s):  
Gülhan Benli
Keyword(s):  
Protected Areas ◽  
Point Cloud ◽  
Laser Scanning ◽  
Three Dimensional ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Laser Scanners ◽  
Point Data

Since the 2000s, terrestrial laser scanning, as one of the methods used to document historical edifices in protected areas, has taken on greater importance because it mitigates the difficulties associated with working on large areas and saves time while also making it possible to better understand all the particularities of the area. Through this technology, comprehensive point data (point clouds) about the surface of an object can be generated in a highly accurate three-dimensional manner. Furthermore, with the proper software this three-dimensional point cloud data can be transformed into three-dimensional rendering/mapping/modeling and quantitative orthophotographs. In this chapter, the study will present the results of terrestrial laser scanning and surveying which was used to obtain three-dimensional point clouds through three-dimensional survey measurements and scans of silhouettes of streets in Fatih in Historic Peninsula in Istanbul, which were then transposed into survey images and drawings. The study will also cite examples of the facade mapping using terrestrial laser scanning data in Istanbul Historic Peninsula Project.

scholarly journals Estimating Changes in Forest Attributes and Enhancing Growth Projections: a Review of Existing Approaches and Future Directions Using Airborne 3D Point Cloud Data

2021 ◽  
Vol 7 (1) ◽  
pp. 1-24
Author(s):  
Piotr Tompalski ◽  
Nicholas C. Coops ◽  
Joanne C. White ◽  
Tristan R.H. Goodbody ◽  
Chris R. Hennigar ◽  
...  
Keyword(s):  
Forest Inventory ◽  
Point Cloud ◽  
Laser Scanning ◽  
Growth Models ◽  
Three Dimensional ◽  
Point Clouds ◽  
Forest Growth ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Aerial Photogrammetry

Abstract Purpose of Review The increasing availability of three-dimensional point clouds, including both airborne laser scanning and digital aerial photogrammetry, allow for the derivation of forest inventory information with a high level of attribute accuracy and spatial detail. When available at two points in time, point cloud datasets offer a rich source of information for detailed analysis of change in forest structure. Recent Findings Existing research across a broad range of forest types has demonstrated that those analyses can be performed using different approaches, levels of detail, or source data. By reviewing the relevant findings, we highlight the potential that bi- and multi-temporal point clouds have for enhanced analysis of forest growth. We divide the existing approaches into two broad categories— – approaches that focus on estimating change based on predictions of two or more forest inventory attributes over time, and approaches for forecasting forest inventory attributes. We describe how point clouds acquired at two or more points in time can be used for both categories of analysis by comparing input airborne datasets, before discussing the methods that were used, and resulting accuracies. Summary To conclude, we outline outstanding research gaps that require further investigation, including the need for an improved understanding of which three-dimensional datasets can be applied using certain methods. We also discuss the likely implications of these datasets on the expected outcomes, improvements in tree-to-tree matching and analysis, integration with growth simulators, and ultimately, the development of growth models driven entirely with point cloud data.

scholarly journals Fused Transparent Visualization of Point Cloud Data and Background Photographic Image for Tangible Cultural Heritage Assets

2019 ◽  
Vol 8 (8) ◽  
pp. 343 ◽  
Author(s):  
Li ◽  
Hasegawa ◽  
Nii ◽  
Tanaka
Keyword(s):  
Cultural Heritage ◽  
Point Cloud ◽  
Laser Scanning ◽  
Large Scale ◽  
Three Dimensional ◽  
Point Clouds ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Photographic Image ◽  
Scale Point

Digital archiving of three-dimensional cultural heritage assets has increased the demand for visualization of large-scale point clouds of cultural heritage assets acquired by laser scanning. We proposed a fused transparent visualization method that visualizes a point cloud of a cultural heritage asset in an environment using a photographic image as the background. We also proposed lightness adjustment and color enhancement methods to deal with the reduced visibility caused by the fused visualization. We applied the proposed method to a laser-scanned point cloud of a high-valued cultural festival float with complex inner and outer structures. Experimental results demonstrate that the proposed method enables high-quality transparent visualization of the cultural asset in its surrounding environment.

scholarly journals Automatic Recognition of Pole-Like Objects from Mobile Laser Scanning Point Clouds

2018 ◽  
Vol 10 (12) ◽  
pp. 1891 ◽  
Author(s):  
Zhenwei Shi ◽  
Zhizhong Kang ◽  
Yi Lin ◽  
Yu Liu ◽  
Wei Chen
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Shape Matching ◽  
Feature Detection ◽  
Three Dimensional ◽  
Point Clouds ◽  
Information Support ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Linear Feature

Mobile Laser Scanning (MLS) point cloud data contains rich three-dimensional (3D) information on road ancillary facilities such as street lamps, traffic signs and utility poles. Automatically recognizing such information from point cloud would provide benefits for road safety inspection, ancillary facilities management and so on, and can also provide basic information support for the construction of an information city. This paper presents a method for extracting and classifying pole-like objects (PLOs) from unstructured MLS point cloud data. Firstly, point cloud is preprocessed to remove outliers, downsample and filter ground points. Then, the PLOs are extracted from the point cloud by spatial independence analysis and cylindrical or linear feature detection. Finally, the PLOs are automatically classified by 3D shape matching. The method was tested based on two point clouds with different road environments. The completeness, correctness and overall accuracy were 92.7%, 97.4% and 92.3% respectively in Data I. For Data II, that provided by International Society for Photogrammetry and Remote Sensing Working Group (ISPRS WG) III/5 was also used to test the performance of the method, and the completeness, correctness and overall accuracy were 90.5%, 97.1% and 91.3%, respectively. Experimental results illustrate that the proposed method can effectively extract and classify PLOs accurately and effectively, which also shows great potential for further applications of MLS point cloud data.

scholarly journals A METHOD FOR EXTRACTING SUBSTATION EQUIPMENT BASED ON UAV LASER SCANNING POINT CLOUDS

2020 ◽  
Vol XLIV-4/W3-2020 ◽  
pp. 413-419
Author(s):  
W. Yu ◽  
J. Xi ◽  
Z. Wu ◽  
W. Lei ◽  
C. Zhu ◽  
...  
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Three Dimensional ◽  
Point Clouds ◽  
Dimensional Structure ◽  
Scanning System ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Substation Equipment ◽  
Voxel Grid

Abstract. Smart grid construction puts higher demands on the construction of 3D models of substations. However, duo to the complex and diverse structures of substation facilities, it is still a challenge to extract the fine three-dimensional structure of the substation facilities from the massive laser point clouds. To solve this problem, this paper proposes a method for extracting substation equipment from laser scanning point clouds. Firstly, in order to improve the processing efficiency and reduce the noises, the regular voxel grid sampling method is used to down-sample the input point cloud. Furthermore, the multi-scale morphological filtering algorithm is used to segment the point cloud into ground points and non-ground points. Based on the non-ground point cloud data, the substation region is extracted using plane detection in point clouds. Then, for the filtered substation point cloud data, a three-dimensional polygon prism segmentation algorithm based on point dimension feature is proposed to extract the substation equipment. Finally, the substation LiDAR point cloud data collected by the UAV laser scanning system is used to verify the algorithm, and the qualitative and quantitative comparison analysis between the detected results and the manually extracted results are carried out. The experimental results show that the proposed method can accurately extract the substation equipment structure from the laser point cloud data. The results are consistent with the manually extracted results, which demonstrate the great potential of the proposed method in substation extraction and power system 3D modelling applications.

scholarly journals Multi-Dimensional Underwater Point Cloud Detection Based on Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 884
Author(s):  
Chia-Ming Tsai ◽  
Yi-Horng Lai ◽  
Yung-Da Sun ◽  
Yu-Jen Chung ◽  
Jau-Woei Perng
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Three Dimensional ◽  
Point Clouds ◽  
Training Data ◽  
Network Architectures ◽  
Point Cloud Data ◽  
Data Types ◽  
Raw Data ◽  
Cloud Data

Numerous sensors can obtain images or point cloud data on land, however, the rapid attenuation of electromagnetic signals and the lack of light in water have been observed to restrict sensing functions. This study expands the utilization of two- and three-dimensional detection technologies in underwater applications to detect abandoned tires. A three-dimensional acoustic sensor, the BV5000, is used in this study to collect underwater point cloud data. Some pre-processing steps are proposed to remove noise and the seabed from raw data. Point clouds are then processed to obtain two data types: a 2D image and a 3D point cloud. Deep learning methods with different dimensions are used to train the models. In the two-dimensional method, the point cloud is transferred into a bird’s eye view image. The Faster R-CNN and YOLOv3 network architectures are used to detect tires. Meanwhile, in the three-dimensional method, the point cloud associated with a tire is cut out from the raw data and is used as training data. The PointNet and PointConv network architectures are then used for tire classification. The results show that both approaches provide good accuracy.

scholarly journals 3D COMPARISON TOWARDS A COMPREHENSIVE ANALYSIS OF A BUILDING IN CULTURAL HERITAGE

2018 ◽  
Vol XLII-2 ◽  
pp. 1061-1066 ◽  
Author(s):  
I. Selvaggi ◽  
M. Dellapasqua ◽  
F. Franci ◽  
A. Spangher ◽  
D. Visintini ◽  
...  
Keyword(s):  
Cultural Heritage ◽  
Point Cloud ◽  
Laser Scanning ◽  
Three Dimensional ◽  
Deformation Analysis ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Site Restoration ◽  
Close Range ◽  
Remote Sensing Techniques

Terrestrial remote sensing techniques, including both Terrestrial Laser Scanning (TLS) and Close-Range Photogrammetry (CRP), have been recently used in multiple applications and projects with particular reference to the documentation/inspection of a wide variety of Cultural Heritage structures.<br> The high density of TLS point cloud data allows to perform structure survey in an unprecedented level of detail, providing a direct solution for the digital three-dimensional modelling, the site restoration and the analysis of the structural conditions. Textural information provided by CRP can be used for the photorealistic representation of the surveyed structure. With respect to many studies, the combination of TLS and CRP techniques produces the best results for Cultural Heritage documentation purposes. Moreover, TLS and CRP point cloud data have been proved to be useful in the field of deformation analysis and structural health monitoring. They can be the input data for the Finite Element Method (FEM), providing some prior knowledge concerning the material and the boundary conditions such as constraints and loading.<br> The paper investigates the capabilities and advantages of TLS and CRP data integration for the three-dimensional modelling compared to a simplified geometric reconstruction. This work presents some results concerning the Baptistery of Aquileia in Italy, characterized by an octagonal plan and walls composed by masonry stones with good texture.

scholarly journals ROBUST CYLINDER FITTING IN THREE-DIMENSIONAL POINT CLOUD DATA

2017 ◽  
Vol XLII-1/W1 ◽  
pp. 63-70 ◽  
Author(s):  
A. Nurunnabi ◽  
Y. Sadahiro ◽  
R. Lindenbergh
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Robust Regression ◽  
Three Dimensional ◽  
Principal Component ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Fitting Algorithm ◽  
Fitting In ◽  
Cylinder Fitting

This paper investigates the problems of cylinder fitting in laser scanning three-dimensional Point Cloud Data (PCD). Most existing methods require full cylinder data, do not study the presence of outliers, and are not statistically robust. But especially mobile laser scanning often has incomplete data, as street poles for example are only scanned from the road. Moreover, existence of outliers is common. Outliers may occur as random or systematic errors, and may be scattered and/or clustered. In this paper, we present a statistically robust cylinder fitting algorithm for PCD that combines Robust Principal Component Analysis (RPCA) with robust regression. Robust principal components as obtained by RPCA allow estimating cylinder directions more accurately, and an existing efficient circle fitting algorithm following robust regression principles, properly fit cylinder. We demonstrate the performance of the proposed method on artificial and real PCD. Results show that the proposed method provides more accurate and robust results: (i) in the presence of noise and high percentage of outliers, (ii) for incomplete as well as complete data, (iii) for small and large number of points, and (iv) for different sizes of radius. On 1000 simulated quarter cylinders of 1m radius with 10% outliers a PCA based method fit cylinders with a radius of on average 3.63 meter (m); the proposed method on the other hand fit cylinders of on average 1.02&amp;thinsp;m radius. The algorithm has potential in applications such as fitting cylindrical (e.g., light and traffic) poles, diameter at breast height estimation for trees, and building and bridge information modelling.

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