scholarly journals INTERACTIVE CLASSIFICATION OF CONSTRUCTION MATERIALS: FEEDBACK DRIVEN FRAMEWORK FOR ANNOTATION AND ANALYSIS OF 3D POINT CLOUDS

2017 ◽  
Vol XLII-2/W5 ◽  
pp. 343-347
Author(s):  
M. R. Hess ◽  
V. Petrovic ◽  
F. Kuester
Keyword(s):  
Cultural Heritage ◽  
Point Cloud ◽  
Laser Scanning ◽  
Building Materials ◽  
Point Clouds ◽  
Normal Vector ◽  
Surface Geometry ◽  
Historical Structures ◽  
3D Point Clouds

Digital documentation of cultural heritage structures is increasingly more common through the application of different imaging techniques. Many works have focused on the application of laser scanning and photogrammetry techniques for the acquisition of threedimensional (3D) geometry detailing cultural heritage sites and structures. With an abundance of these 3D data assets, there must be a digital environment where these data can be visualized and analyzed. Presented here is a feedback driven visualization framework that seamlessly enables interactive exploration and manipulation of massive point cloud data. The focus of this work is on the classification of different building materials with the goal of building more accurate as-built information models of historical structures. User defined functions have been tested within the interactive point cloud visualization framework to evaluate automated and semi-automated classification of 3D point data. These functions include decisions based on observed color, laser intensity, normal vector or local surface geometry. Multiple case studies are presented here to demonstrate the flexibility and utility of the presented point cloud visualization framework to achieve classification objectives.

scholarly journals FEATURE RELEVANCE ANALYSIS FOR 3D POINT CLOUD CLASSIFICATION USING DEEP LEARNING

2019 ◽  
Vol IV-2/W5 ◽  
pp. 373-380 ◽  
Author(s):  
A. Kumar ◽  
K. Anders ◽  
L Winiwarter ◽  
B. Höfle
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Normal Vector ◽  
3D Point Cloud ◽  
Point Distribution ◽  
Spatial Point ◽  
3D Point Clouds ◽  
Normal Vectors

<p><strong>Abstract.</strong> 3D point clouds acquired by laser scanning and other techniques are difficult to interpret because of their irregular structure. To make sense of this data and to allow for the derivation of useful information, a segmentation of the points in groups, units, or classes fit for the specific use case is required. In this paper, we present a non-end-to-end deep learning classifier for 3D point clouds using multiple sets of input features and compare it with an implementation of the state-of-the-art deep learning framework PointNet++. We first start by extracting features derived from the local normal vector (normal vectors, eigenvalues, and eigenvectors) from the point cloud, and study the result of classification for different local search radii. We extract additional features related to spatial point distribution and use them together with the normal vector-based features. We find that the classification accuracy improves by up to 33% as we include normal vector features with multiple search radii and features related to spatial point distribution. Our method achieves a mean Intersection over Union (mIoU) of 94% outperforming PointNet++’s Multi Scale Grouping by up to 12%. The study presents the importance of multiple search radii for different point cloud features for classification in an urban 3D point cloud scene acquired by terrestrial laser scanning.</p>

scholarly journals Symmetry Analysis of Oriental Polygonal Pagodas Using 3D Point Clouds for Cultural Heritage

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1228
Author(s):  
Ting On Chan ◽  
Linyuan Xia ◽  
Yimin Chen ◽  
Wei Lang ◽  
Tingting Chen ◽  
...  
Keyword(s):  
Cultural Heritage ◽  
Unmanned Aerial Vehicle ◽  
Point Cloud ◽  
Geometric Model ◽  
Digital Camera ◽  
Point Clouds ◽  
Symmetry Analysis ◽  
3D Point Clouds ◽  
Transmission Towers ◽  
Aerial Vehicle

Ancient pagodas are usually parts of hot tourist spots in many oriental countries due to their unique historical backgrounds. They are usually polygonal structures comprised by multiple floors, which are separated by eaves. In this paper, we propose a new method to investigate both the rotational and reflectional symmetry of such polygonal pagodas through developing novel geometric models to fit to the 3D point clouds obtained from photogrammetric reconstruction. The geometric model consists of multiple polygonal pyramid/prism models but has a common central axis. The method was verified by four datasets collected by an unmanned aerial vehicle (UAV) and a hand-held digital camera. The results indicate that the models fit accurately to the pagodas’ point clouds. The symmetry was realized by rotating and reflecting the pagodas’ point clouds after a complete leveling of the point cloud was achieved using the estimated central axes. The results show that there are RMSEs of 5.04 cm and 5.20 cm deviated from the perfect (theoretical) rotational and reflectional symmetries, respectively. This concludes that the examined pagodas are highly symmetric, both rotationally and reflectionally. The concept presented in the paper not only work for polygonal pagodas, but it can also be readily transformed and implemented for other applications for other pagoda-like objects such as transmission towers.

scholarly journals ON A KNOWLEDGE-BASED APPROACH TO THE CLASSIFICATION OF MOBILE LASER SCANNING POINT CLOUDS

2018 ◽  
Vol XLII-4 ◽  
pp. 343-349 ◽  
Author(s):  
M. Lemmens
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Decision Rules ◽  
Point Clouds ◽  
Bench Mark ◽  
Automated Classification ◽  
Initial Experiment ◽  
Data Set ◽  
Knowledge Based

<p><strong>Abstract.</strong> A knowledge-based system exploits the knowledge, which a human expert uses for completing a complex task, through a database containing decision rules, and an inference engine. Already in the early nineties knowledge-based systems have been proposed for automated image classification. Lack of success faded out initial interest and enthusiasm, the same fate neural networks struck at that time. Today the latter enjoy a steady revival. This paper aims at demonstrating that a knowledge-based approach to automated classification of mobile laser scanning point clouds has promising prospects. An initial experiment exploiting only two features, height and reflectance value, resulted in an overall accuracy of 79<span class="thinspace"></span>% for the Paris-rue-Madame point cloud bench mark data set.</p>

scholarly journals GEOMETRIC FEATURES ANALYSIS FOR THE CLASSIFICATION OF CULTURAL HERITAGE POINT CLOUDS

2019 ◽  
Vol XLII-2/W15 ◽  
pp. 541-548 ◽  
Author(s):  
E. Grilli ◽  
E. M. Farella ◽  
A. Torresani ◽  
F. Remondino
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cultural Heritage ◽  
Point Clouds ◽  
Geometric Features ◽  
3D Point Clouds ◽  
3D Data ◽  
3D Documentation ◽  
The Impact

<p><strong>Abstract.</strong> In the last years, the application of artificial intelligence (Machine Learning and Deep Learning methods) for the classification of 3D point clouds has become an important task in modern 3D documentation and modelling applications. The identification of proper geometric and radiometric features becomes fundamental to classify 2D/3D data correctly. While many studies have been conducted in the geospatial field, the cultural heritage sector is still partly unexplored. In this paper we analyse the efficacy of the geometric covariance features as a support for the classification of Cultural Heritage point clouds. To analyse the impact of the different features calculated on spherical neighbourhoods at various radius sizes, we present results obtained on four different heritage case studies using different features configurations.</p>

scholarly journals Special Issue on “Terrestrial Laser Scanning”: Editors’ Notes

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4569
Author(s):  
Joan R. Rosell-Polo ◽  
Eduard Gregorio ◽  
Jordi Llorens
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
Special Issue ◽  
Alternative Technologies ◽  
Cloud Processing ◽  
3D Point Clouds ◽  
Point Cloud Processing

In this editorial, we provide an overview of the content of the special issue on “Terrestrial Laser Scanning”. The aim of this Special Issue is to bring together innovative developments and applications of terrestrial laser scanning (TLS), understood in a broad sense. Thus, although most contributions mainly involve the use of laser-based systems, other alternative technologies that also allow for obtaining 3D point clouds for the measurement and the 3D characterization of terrestrial targets, such as photogrammetry, are also considered. The 15 published contributions are mainly focused on the applications of TLS to the following three topics: TLS performance and point cloud processing, applications to civil engineering, and applications to plant characterization.

scholarly journals Comparing Machine and Deep Learning Methods for Large 3D Heritage Semantic Segmentation

2020 ◽  
Vol 9 (9) ◽  
pp. 535
Author(s):  
Francesca Matrone ◽  
Eleonora Grilli ◽  
Massimo Martini ◽  
Marina Paolanti ◽  
Roberto Pierdicca ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cultural Heritage ◽  
Laser Scanning ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Classification Algorithms ◽  
Learning Methods ◽  
3D Point Clouds ◽  
The Subject

In recent years semantic segmentation of 3D point clouds has been an argument that involves different fields of application. Cultural heritage scenarios have become the subject of this study mainly thanks to the development of photogrammetry and laser scanning techniques. Classification algorithms based on machine and deep learning methods allow to process huge amounts of data as 3D point clouds. In this context, the aim of this paper is to make a comparison between machine and deep learning methods for large 3D cultural heritage classification. Then, considering the best performances of both techniques, it proposes an architecture named DGCNN-Mod+3Dfeat that combines the positive aspects and advantages of these two methodologies for semantic segmentation of cultural heritage point clouds. To demonstrate the validity of our idea, several experiments from the ArCH benchmark are reported and commented.

scholarly journals Automating Parameter Learning for Classifying Terrestrial LiDAR Point Cloud Using 2D Land Cover Maps

2018 ◽  
Vol 10 (8) ◽  
pp. 1192 ◽  
Author(s):  
Chen-Chieh Feng ◽  
Zhou Guo
Keyword(s):  
Land Cover ◽  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Parameter Learning ◽  
Cloud Classification ◽  
Land Cover Map ◽  
Point Cloud Classification ◽  
Optimal Feature

The automating classification of point clouds capturing urban scenes is critical for supporting applications that demand three-dimensional (3D) models. Achieving this goal, however, is met with challenges because of the varying densities of the point clouds and the complexity of the 3D data. In order to increase the level of automation in the point cloud classification, this study proposes a segment-based parameter learning method that incorporates a two-dimensional (2D) land cover map, in which a strategy of fusing the 2D land cover map and the 3D points is first adopted to create labelled samples, and a formalized procedure is then implemented to automatically learn the following parameters of point cloud classification: the optimal scale of the neighborhood for segmentation, optimal feature set, and the training classifier. It comprises four main steps, namely: (1) point cloud segmentation; (2) sample selection; (3) optimal feature set selection; and (4) point cloud classification. Three datasets containing the point cloud data were used in this study to validate the efficiency of the proposed method. The first two datasets cover two areas of the National University of Singapore (NUS) campus while the third dataset is a widely used benchmark point cloud dataset of Oakland, Pennsylvania. The classification parameters were learned from the first dataset consisting of a terrestrial laser-scanning data and a 2D land cover map, and were subsequently used to classify both of the NUS datasets. The evaluation of the classification results showed overall accuracies of 94.07% and 91.13%, respectively, indicating that the transition of the knowledge learned from one dataset to another was satisfactory. The classification of the Oakland dataset achieved an overall accuracy of 97.08%, which further verified the transferability of the proposed approach. An experiment of the point-based classification was also conducted on the first dataset and the result was compared to that of the segment-based classification. The evaluation revealed that the overall accuracy of the segment-based classification is indeed higher than that of the point-based classification, demonstrating the advantage of the segment-based approaches.

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 AUTOMATIC INTEGRATION OF LASER SCANNING AND PHOTOGRAMMETRIC POINT CLOUDS: FROM ACQUISITION TO CO-REGISTRATION

2021 ◽  
Vol XLIII-B1-2021 ◽  
pp. 85-92
Author(s):  
T. Partovi ◽  
M. Dähne ◽  
M. Maboudi ◽  
D. Krueger ◽  
M. Gerke
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
High Capacity ◽  
Point Clouds ◽  
Complex Objects ◽  
Initial Image ◽  
Joint Project ◽  
3D Point Clouds ◽  
Image Capturing ◽  
Data Capturing

Abstract. Laser scanning systems have been developed to capture very high-resolution 3D point clouds and consequently acquire the object geometry. This object measuring technique has a high capacity for being utilized in a wide variety of applications such as indoor and outdoor modelling. The Terrestrial Laser Scanning (TLS) is used as an important data capturing measurement system to provide high quality point cloud from industrial or built-up environments. However, the static nature of the TLS and complexity of the industrial sites necessitate employing a complementary data capturing system e.g. cameras to fill the gaps in the TLS point cloud caused by occlusions which is very common in complex industrial areas. Moreover, employing images provide better radiometric and edge information. This motivated a joint project to develop a system for automatic and robust co-registration of TLS data and images directly, especially for complex objects. In this paper, the proposed methods for various components of this project including gap detection from point cloud, calculation of initial image capturing configuration, user interface and support system for the image capturing procedures, and co-registration between TLS point cloud and photogrammetric point cloud are presented. The primarily results on a complex industrial environment are promising.

scholarly journals INDOOR MAPPING OF A COMPLEX CULTURAL HERITAGE SCENE USING TLS AND HMLS LASER SCANNING

2021 ◽  
Vol XLIII-B2-2021 ◽  
pp. 605-612
Author(s):  
V. E. Oniga ◽  
A. I. Breaban ◽  
E. I. Alexe ◽  
C. Văsii
Keyword(s):  
Standard Deviation ◽  
Cultural Heritage ◽  
Long Range ◽  
Interior Design ◽  
Point Cloud ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Point Clouds ◽  
Acquisition Time ◽  
Wide Range

Abstract. Indoor mapping and modelling is an important research subject with application in a wide range of domains including interior design, real estate, cultural heritage conservation and restoration. There are multiple sensors applicable for 3D indoor modelling, but the laser scanning technique is frequently used because of the acquisition time, detailed information and accuracy. In this paper, the efficiency of the Maptek I-Site 8820 terrestrial scanner, which is a long-range laser scanner and the accuracy of a HMLS point cloud acquired with a mobile scanner, namely GeoSlam Zeb Horizon were tested for indoor mapping. Aula Magna “Carmen Silva” of the “Gheorghe Asachi” Technical University of Iasi is studied in the current paper since the auditorium interior creates a distinct environment that combines complex geometric structures with architectural lighting and for preserving its great cultural value, the monument has a national historical significance. The registration process of the TLS point clouds was done using two methods: a semi-automatic one with artificial targets and a completely automatic one, based on Iterative Closest Point (ICP) algorithm. The resulted TLS point cloud was analysed in relation to the HMLS point cloud by computing the M3C2 (Multiscale Model to Model Cloud Comparison), obtaining a standard deviation of 2.1 cm and by investigating the Hausdorff distances from which resulted a standard deviation (σ) of 1.6 cm. Cross-sections have been extracted from the HMLS and TLS point clouds and after comparing the sections, 80% of the sigma values are less or equal to 1 cm. The results show high potential of using HMLS and also a long-range laser scanner for 3D modelling of complex scenes, the occlusion effect in the case of TLS being only 5% of the scanned area.

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