scholarly journals 3D CAMERAS ACQUISITIONS FOR THE DOCUMENTATION OF CULTURAL HERITAGE

2019 ◽  
Vol XLII-2/W9 ◽  
pp. 639-646 ◽  
Author(s):  
M. Pulcrano ◽  
S. Scandurra ◽  
G. Minin ◽  
A. di Luggo
Keyword(s):  
Point Cloud ◽  
Processing Time ◽  
Visual Quality ◽  
Point Clouds ◽  
Careful Planning ◽  
New Techniques ◽  
Built Heritage ◽  
Photorealistic Rendering ◽  
Geometrical Data ◽  
Rotating Camera

<p><strong>Abstract.</strong> Photography has always been considered as a valid tool to acquire information about reality. Nowadays, its versatility, together with the development of new techniques and technologies, allows to use it in different fields of application. Particularly, in the digitization of built heritage, photography not only enables to understand and document historical and architectural artifacts but also to acquire morphological and geometrical data about them with automated digital photogrammetry. Nowadays, photogrammetry enables many tools to give virtual casts of reality by showing it in the way of point cloud. Although they can have metric reliability and visual quality, traditional instruments &amp;ndash; such as monoscopic cameras &amp;ndash; involve a careful planning of the campaign phase and a long acquisition and processing time. On the contrary, the most recent ones, based on the integration of different sensors and cameras, try to reduce the gap between time and results. The latter include some systems of indoor mapping who, thanks to 360&amp;deg; acquisitions and SLAM technology, reconstruct the original scene in real time in great detail and with a photorealistic rendering. This study is aimed at reporting a research evaluating metric reliability and the level of survey detail with a Matterport Pro2 3D motorized rotating camera, equipped with SLAM technology, whose results have been compared with point clouds obtained by image-based and range-based processes.</p>

scholarly journals ORIENTATION-BASED PAIRWISE COARSE REGISTRATION WITH MARKERLESS TERRESTRIAL LASER SCANS

2019 ◽  
Vol XLII-4/W16 ◽  
pp. 425-432
Author(s):  
S. N. Mohd Isa ◽  
S. A. Abdul Shukor ◽  
N. A. Rahim ◽  
I. Maarof ◽  
Z. R. Yahya ◽  
...  
Keyword(s):  
Point Cloud ◽  
Processing Time ◽  
Point Clouds ◽  
Relative Orientation ◽  
Point Cloud Data ◽  
Data Set ◽  
Cloud Data ◽  
Overlapping Data ◽  
Coarse Registration ◽  
Voxel Grid

Abstract. In this paper, pairwise coarse registration is presented using real world point cloud data obtained by terrestrial laser scanner and without information on reference marker on the scene. The challenge in the data is because of multi-scanning which caused large data size in millions of points due to limited range about the scene generated from side view. Furthermore, the data have a low percentage of overlapping between two scans, and the point cloud data were acquired from structures with geometrical symmetry which leads to minimal transformation during registration process. To process the data, 3D Harris keypoint is used and coarse registration is done by Iterative Closest Point (ICP). Different sampling methods were applied in order to evaluate processing time for further analysis on different voxel grid size. Then, Root Means Squared Error (RMSE) is used to determine the accuracy of the approach and to study its relation to relative orientation of scan by pairwise registration. The results show that the grid average downsampling method gives shorter processing time with reasonable RMSE in finding the exact scan pair. It can also be seen that grid step size is having an inverse relationship with downsampling points. This setting is used to test on smaller overlapping data set of other heritage building. Evaluation on relative orientation is studied from transformation parameter for both data set, where Data set I, which higher overlapping data gives better accuracy which may be due to the small distance between the two point clouds compared to Data set II.

scholarly journals SCAN PLANNING OPTIMIZATION FOR OUTDOOR ARCHAEOLOGICAL SITES

2019 ◽  
Vol XLII-2/W11 ◽  
pp. 489-494
Author(s):  
L. Díaz-Vilariño ◽  
E. Frías ◽  
M. Previtali ◽  
M. Scaioni ◽  
J. Balado
Keyword(s):  
Point Cloud ◽  
Laser Scanner ◽  
Point Clouds ◽  
Data Capture ◽  
Archaeological Sites ◽  
High Quality ◽  
Large Size ◽  
Planning Optimization ◽  
Geometrical Data ◽  
Grid Based

<p><strong>Abstract.</strong> The protection and management of archaeological sites require from a deep documentation and analysis, and although hand measuring and documentation is the cheapest way for collecting data, laser scanner has been gradually integrated for the geometrical data capture since point clouds have a high quality in terms of accuracy, precision and resolution. Although acquisition with laser scanner is considered a quick process, scan planning is of high relevance when considering outdoor archaeological sites because of their large size and complexity. In this paper, an automatic methodology to optimize the number and position of scans in order to obtain a point cloud of high quality in terms of data completeness is proposed. The aim of the methodology is to minimize the number of scans, minimizing at the same time the estimated surveying time and the amount of repetitive acquired data. Scan candidates are generated by using a grid-based and a triangulation-based distribution, and results show a faster analysis when triangulation is implemented. The methodology is tested into two real case studies from Italy and Spain, showing the applicability of scan planning in archaeological sites.</p>

An efficient simplification method for point cloud based on salient regions detection

2019 ◽  
Vol 53 (2) ◽  
pp. 487-504 ◽  
Author(s):  
Abdul Rahman El Sayed ◽  
Abdallah El Chakik ◽  
Hassan Alabboud ◽  
Adnan Yassine
Keyword(s):  
Point Cloud ◽  
Processing Time ◽  
Point Clouds ◽  
The Other ◽  
Point Cloud Data ◽  
Weighted Graphs ◽  
Cloud Data ◽  
3D Point Clouds ◽  
Salient Regions ◽  
The Stability

Many computer vision approaches for point clouds processing consider 3D simplification as an important preprocessing phase. On the other hand, the big amount of point cloud data that describe a 3D object require excessively a large storage and long processing time. In this paper, we present an efficient simplification method for 3D point clouds using weighted graphs representation that optimizes the point clouds and maintain the characteristics of the initial data. This method detects the features regions that describe the geometry of the surface. These features regions are detected using the saliency degree of vertices. Then, we define features points in each feature region and remove redundant vertices. Finally, we will show the robustness of our methodviadifferent experimental results. Moreover, we will study the stability of our method according to noise.

scholarly journals Two-Layer-Graph Clustering for Real-Time 3D LiDAR Point Cloud Segmentation

2020 ◽  
Vol 10 (23) ◽  
pp. 8534
Author(s):  
Haozhe Yang ◽  
Zhiling Wang ◽  
Linglong Lin ◽  
Huawei Liang ◽  
Weixin Huang ◽  
...  
Keyword(s):  
Real Time ◽  
Autonomous Vehicles ◽  
Point Cloud ◽  
Processing Time ◽  
Autonomous Vehicle ◽  
Point Clouds ◽  
Complex Objects ◽  
Time Performance ◽  
Sensor Position ◽  
Perception System

The perception system has become a topic of great importance for autonomous vehicles, as high accuracy and real-time performance can ensure safety in complex urban scenarios. Clustering is a fundamental step for parsing point cloud due to the extensive input data (over 100,000 points) of a wide variety of complex objects. It is still challenging to achieve high precision real-time performance with limited vehicle-mounted computing resources, which need to balance the accuracy and processing time. We propose a method based on a Two-Layer-Graph (TLG) structure, which can be applied in a real autonomous vehicle under urban scenarios. TLG can describe the point clouds hierarchically, we use a range graph to represent point clouds and a set graph for point cloud sets, which reduce both processing time and memory consumption. In the range graph, Euclidean distance and the angle of the sensor position with two adjacent vectors (calculated from continuing points to different direction) are used as the segmentation standard, which use the local concave features to distinguish different objects close to each other. In the set graph, we use the start and end position to express the whole set of continuous points concisely, and an improved Breadth-First-Search (BFS) algorithm is designed to update categories of point cloud sets between different channels. This method is evaluated on real vehicles and major datasets. The results show that TLG succeeds in providing a real-time performance (less than 20 ms per frame), and a high segmentation accuracy rate (93.64%) for traffic objects in the road of urban scenarios.

scholarly journals A Built Heritage Information System Based on Point Cloud Data: HIS-PC

2020 ◽  
Vol 9 (10) ◽  
pp. 588
Author(s):  
Florent Poux ◽  
Roland Billen ◽  
Jean-Paul Kasprzyk ◽  
Pierre-Henri Lefebvre ◽  
Pierre Hallot
Keyword(s):  
Information System ◽  
Point Cloud ◽  
Point Clouds ◽  
Archaeological Site ◽  
Point Cloud Data ◽  
Data Set ◽  
Cloud Data ◽  
Built Heritage ◽  
Wide Range ◽  
Common Framework

The digital management of an archaeological site requires to store, organise, access and represent all the information that is collected on the field. Heritage building information modelling, archaeological or heritage information systems now tend to propose a common framework where all the materials are managed from a central database and visualised through a 3D representation. In this research, we offer the development of a built heritage information system prototype based on a high-resolution 3D point cloud data set. The particularity of the approach is to consider a user-centred development methodology while avoiding meshing/down-sampling operations. The proposed system is initiated by a close collaboration between multi-modal users (managers, visitors, curators) and a development team (designers, developers, architects). The developed heritage information system permits the management of spatial and temporal information, including a wide range of semantics using relational along with NoSQL databases. The semantics used to describe the artifacts are subject to conceptual modelling. Finally, the system proposes a bi-directional communication with a 3D interface able to stream massive point clouds, which is a big step forward to provide a comprehensive site representation for stakeholders while minimising modelling costs.

Special Issue on Large-Scale Point Cloud Processing

2018 ◽  
Vol 12 (3) ◽  
pp. 327-327
Author(s):  
Hiroshi Masuda ◽  
Hiroaki Date
Keyword(s):  
Point Cloud ◽  
Large Scale ◽  
Point Clouds ◽  
Special Issue ◽  
New Techniques ◽  
Accuracy Measurement ◽  
Cloud Processing ◽  
Point Cloud Processing ◽  
Laser Scanners ◽  
Scale Point

Recently, terrestrial laser scanners have been significantly improved in terms of accuracy, measurement distance, measurement speed, and resolution. They enable us to capture dense 3D point clouds of large-scale objects and fields, such as factories, engineering plants, large equipment, and transport ships. In addition, the mobile mapping system, which is a vehicle equipped with laser scanners and GPSs, can be used for capturing large-scale point clouds from a wide range of roads, buildings, and roadside objects. Large-scale point clouds are useful in a variety of applications, such as renovation and maintenance of facilities, engineering simulation, asset management, and 3D mapping. To realize these applications, new techniques must be developed for processing large-scale point clouds. So far, point processing has been studied mainly for relatively small objects in the field of computer-aided design and computer graphics. However, in recent years, the application areas of point clouds are not limited to conventional domains, but also include manufacturing, civil engineering, construction, transportation, forestry, and so on. This is because the state-of-the-art laser scanner can be used to represent large objects or fields as dense point clouds. We believe that discussing new techniques and applications related to large-scale point clouds beyond the boundaries of traditional academic fields is very important.This special issue addresses the latest research advances in large-scale point cloud processing. This covers a wide area of point processing, including shape reconstruction, geometry processing, object recognition, registration, visualization, and applications. The papers will help readers explore and share their knowledge and experience in technologies and development techniques.All papers were refereed through careful peer reviews. We would like to express our sincere appreciation to the authors for their submissions and to the reviewers for their invaluable efforts for ensuring the success of this special issue.

Registration and Fusion of UAV LiDAR System Sequence Images and Laser Point Clouds

2020 ◽  
Author(s):  
Jiayong Yu ◽  
Longchen Ma ◽  
Maoyi Tian, ◽  
Xiushan Lu
Keyword(s):  
Point Cloud ◽  
Image Data ◽  
Point Clouds ◽  
Optical Image ◽  
Point Cloud Data ◽  
Feature Points ◽  
Lidar System ◽  
Registration Accuracy ◽  
Cloud Data ◽  
Intensity Image

The unmanned aerial vehicle (UAV)-mounted mobile LiDAR system (ULS) is widely used for geomatics owing to its efficient data acquisition and convenient operation. However, due to limited carrying capacity of a UAV, sensors integrated in the ULS should be small and lightweight, which results in decrease in the density of the collected scanning points. This affects registration between image data and point cloud data. To address this issue, the authors propose a method for registering and fusing ULS sequence images and laser point clouds, wherein they convert the problem of registering point cloud data and image data into a problem of matching feature points between the two images. First, a point cloud is selected to produce an intensity image. Subsequently, the corresponding feature points of the intensity image and the optical image are matched, and exterior orientation parameters are solved using a collinear equation based on image position and orientation. Finally, the sequence images are fused with the laser point cloud, based on the Global Navigation Satellite System (GNSS) time index of the optical image, to generate a true color point cloud. The experimental results show the higher registration accuracy and fusion speed of the proposed method, thereby demonstrating its accuracy and effectiveness.

scholarly journals 3D Thermal Monitoring of Jointed Rock Masses through Infrared Thermography and Photogrammetry

2021 ◽  
Vol 13 (5) ◽  
pp. 957
Author(s):  
Guglielmo Grechi ◽  
Matteo Fiorucci ◽  
Gian Marco Marmoni ◽  
Salvatore Martino
Keyword(s):  
Surface Temperature ◽  
Infrared Thermography ◽  
Point Cloud ◽  
Engineering Geology ◽  
Point Clouds ◽  
Jointed Rock ◽  
Geometric Accuracy ◽  
Rock Masses ◽  
Natural Systems ◽  
Temperature Distributions

The study of strain effects in thermally-forced rock masses has gathered growing interest from engineering geology researchers in the last decade. In this framework, digital photogrammetry and infrared thermography have become two of the most exploited remote surveying techniques in engineering geology applications because they can provide useful information concerning geomechanical and thermal conditions of these complex natural systems where the mechanical role of joints cannot be neglected. In this paper, a methodology is proposed for generating point clouds of rock masses prone to failure, combining the high geometric accuracy of RGB optical images and the thermal information derived by infrared thermography surveys. Multiple 3D thermal point clouds and a high-resolution RGB point cloud were separately generated and co-registered by acquiring thermograms at different times of the day and in different seasons using commercial software for Structure from Motion and point cloud analysis. Temperature attributes of thermal point clouds were merged with the reference high-resolution optical point cloud to obtain a composite 3D model storing accurate geometric information and multitemporal surface temperature distributions. The quality of merged point clouds was evaluated by comparing temperature distributions derived by 2D thermograms and 3D thermal models, with a view to estimating their accuracy in describing surface thermal fields. Moreover, a preliminary attempt was made to test the feasibility of this approach in investigating the thermal behavior of complex natural systems such as jointed rock masses by analyzing the spatial distribution and temporal evolution of surface temperature ranges under different climatic conditions. The obtained results show that despite the low resolution of the IR sensor, the geometric accuracy and the correspondence between 2D and 3D temperature measurements are high enough to consider 3D thermal point clouds suitable to describe surface temperature distributions and adequate for monitoring purposes of jointed rock mass.

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.

scholarly journals Parametric Surface Modelling for Tea Leaf Point Cloud Based on Non-Uniform Rational Basis Spline Technique

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1304
Author(s):  
Wenchao Wu ◽  
Yongguang Hu ◽  
Yongzong Lu
Keyword(s):  
Point Cloud ◽  
Principal Component ◽  
Point Clouds ◽  
Parametric Surface ◽  
B Spline ◽  
Surface Modelling ◽  
Modelling Method ◽  
3D Architecture ◽  
Tea Leaf ◽  
Fitting Error

Plant leaf 3D architecture changes during growth and shows sensitive response to environmental stresses. In recent years, acquisition and segmentation methods of leaf point cloud developed rapidly, but 3D modelling leaf point clouds has not gained much attention. In this study, a parametric surface modelling method was proposed for accurately fitting tea leaf point cloud. Firstly, principal component analysis was utilized to adjust posture and position of the point cloud. Then, the point cloud was sliced into multiple sections, and some sections were selected to generate a point set to be fitted (PSF). Finally, the PSF was fitted into non-uniform rational B-spline (NURBS) surface. Two methods were developed to generate the ordered PSF and the unordered PSF, respectively. The PSF was firstly fitted as B-spline surface and then was transformed to NURBS form by minimizing fitting error, which was solved by particle swarm optimization (PSO). The fitting error was specified as weighted sum of the root-mean-square error (RMSE) and the maximum value (MV) of Euclidean distances between fitted surface and a subset of the point cloud. The results showed that the proposed modelling method could be used even if the point cloud is largely simplified (RMSE < 1 mm, MV < 2 mm, without performing PSO). Future studies will model wider range of leaves as well as incomplete point cloud.

Sign in / Sign up

Export Citation Format

Share Document