scholarly journals Accuracy Assessment of Cultural Heritage Models Extracting 3D Point Cloud Geometric Features with RPAS SfM-MVS and TLS Techniques

Drones ◽  
2021 ◽  
Vol 5 (4) ◽  
pp. 145
Author(s):  
Alessandra Capolupo
Keyword(s):  
Cultural Heritage ◽  
Point Cloud ◽  
Accuracy Assessment ◽  
Laser Scanner ◽  
Point Clouds ◽  
Geometric Features ◽  
3D Point Cloud ◽  
3D Point Clouds ◽  
Research Activities ◽  
The Impact

A proper classification of 3D point clouds allows fully exploiting data potentiality in assessing and preserving cultural heritage. Point cloud classification workflow is commonly based on the selection and extraction of respective geometric features. Although several research activities have investigated the impact of geometric features on classification outcomes accuracy, only a few works focused on their accuracy and reliability. This paper investigates the accuracy of 3D point cloud geometric features through a statistical analysis based on their corresponding eigenvalues and covariance with the aim of exploiting their effectiveness for cultural heritage classification. The proposed approach was separately applied on two high-quality 3D point clouds of the All Saints’ Monastery of Cuti (Bari, Southern Italy), generated using two competing survey techniques: Remotely Piloted Aircraft System (RPAS) Structure from Motion (SfM) and Multi View Stereo (MVS) techniques and Terrestrial Laser Scanner (TLS). Point cloud compatibility was guaranteed through re-alignment and co-registration of data. The geometric features accuracy obtained by adopting the RPAS digital photogrammetric and TLS models was consequently analyzed and presented. Lastly, a discussion on convergences and divergences of these results is also provided.

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 Towards Efficient Implementation of an Octree for a Large 3D Point Cloud

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4398 ◽  
Author(s):  
Soohee Han
Keyword(s):  
Point Cloud ◽  
Laser Scanner ◽  
Point Clouds ◽  
Main Memory ◽  
3D Point Cloud ◽  
Airborne Laser Scanner ◽  
Memory Efficiency ◽  
3D Point Clouds ◽  
Short Tunnel ◽  
Better Than

The present study introduces an efficient algorithm to construct a file-based octree for a large 3D point cloud. However, the algorithm was very slow compared with a memory-based approach, and got even worse when using a 3D point cloud scanned in longish objects like tunnels and corridors. The defects were addressed by implementing a semi-isometric octree group. The approach implements several semi-isometric octrees in a group, which tightly covers the 3D point cloud, though each octree along with its leaf node still maintains an isometric shape. The proposed approach was tested using three 3D point clouds captured in a long tunnel and a short tunnel by a terrestrial laser scanner, and in an urban area by an airborne laser scanner. The experimental results showed that the performance of the semi-isometric approach was not worse than a memory-based approach, and quite a lot better than a file-based one. Thus, it was proven that the proposed semi-isometric approach achieves a good balance between query performance and memory efficiency. In conclusion, if given enough main memory and using a moderately sized 3D point cloud, a memory-based approach is preferable. When the 3D point cloud is larger than the main memory, a file-based approach seems to be the inevitable choice, however, the semi-isometric approach is the better option.

scholarly journals MULTIPLE USES OF A 3D POINT CLOUD: THE CASTLE OF FRANCHIMONT (PROVINCE OF LIÈGE, BELGIUM)

2017 ◽  
Vol XLII-2/W5 ◽  
pp. 475-481
Author(s):  
A. Luczfalvy Jancsó ◽  
B. Jonlet ◽  
P. Hallot ◽  
P. Hoffsummer ◽  
R. Billen
Keyword(s):  
Information System ◽  
Cultural Heritage ◽  
Point Cloud ◽  
Point Clouds ◽  
3D Point Cloud ◽  
3D Point Clouds ◽  
Multiple Uses ◽  
The Creation ◽  
Made In

This paper presents the identified obstacles, needs and selected solutions for the study of the medieval castle of Franchimont, located in the province of Liège (Belgium). After taking into account the requirements from all the disciplines at work as well as the problems that would have to be tackled, the creation of a 3D point cloud was decided. This solution would be able to deal with the characteristics and needs of a research involving building archaeology and related fields. The decision was made in order to manage all of the available data and to provide a common working tool for every involved cultural heritage actor. To achieve this, the elaboration of an Archaeological Information System based on 3D point clouds as a common virtual workspace is being taken into consideration.

scholarly journals Accuracy assessment of modeling architectural structures and details using terrestrial laser scanning

2015 ◽  
Vol XL-5/W7 ◽  
pp. 241-243 ◽  
Author(s):  
M. Kedzierski ◽  
P. Walczykowski ◽  
A. Orych ◽  
P. Czarnecka
Keyword(s):  
Cultural Heritage ◽  
Point Cloud ◽  
Laser Scanning ◽  
Incidence Angle ◽  
Accuracy Assessment ◽  
Construction Materials ◽  
Point Clouds ◽  
Test Field ◽  
Different Types ◽  
The Impact

One of the most important aspects when performing architectural documentation of cultural heritage structures is the accuracy of both the data and the products which are generated from these data: documentation in the form of 3D models or vector drawings. The paper describes an assessment of the accuracy of modelling data acquired using a terrestrial phase scanner in relation to the density of a point cloud representing the surface of different types of construction materials typical for cultural heritage structures. This analysis includes the impact of the scanning geometry: the incidence angle of the laser beam and the scanning distance. For the purposes of this research, a test field consisting of samples of different types of construction materials (brick, wood, plastic, plaster, a ceramic tile, sheet metal) was built. The study involved conducting measurements at different angles and from a range of distances for chosen scanning densities. Data, acquired in the form of point clouds, were then filtered and modelled. An accuracy assessment of the 3D model was conducted by fitting it with the point cloud. The reflection intensity of each type of material was also analyzed, trying to determine which construction materials have the highest reflectance coefficients, and which have the lowest reflection coefficients, and in turn how this variable changes for different scanning parameters. Additionally measurements were taken of a fragment of a building in order to compare the results obtained in laboratory conditions, with those taken in field conditions.

scholarly journals A DIGITAL ARCHIVE OF BOROBUDUR BASED ON 3D POINT CLOUDS

2021 ◽  
Vol XLIII-B2-2021 ◽  
pp. 577-582
Author(s):  
M. Kawato ◽  
L. Li ◽  
K. Hasegawa ◽  
M. Adachi ◽  
H. Yamaguchi ◽  
...  
Keyword(s):  
Cultural Heritage ◽  
Point Cloud ◽  
Three Dimensional ◽  
Point Clouds ◽  
World Heritage Site ◽  
Digital Archive ◽  
First Person ◽  
3D Point Cloud ◽  
3D Point Clouds ◽  
The Temple

Abstract. Three-dimensional point clouds are becoming popular representations for digital archives of cultural heritage sites. The Borobudur Temple, located in Central Java, Indonesia, was built in the 8th century. Borobudur is considered one of the greatest Buddhist monuments in the world and was listed as a UNESCO World Heritage site. We are developing a virtual reality system as a digital archive of the Borobudur Temple. This research is a collaboration between Ritsumeikan University, Japan, the Indonesian Institute of Sciences (LIPI), and the Borobudur Conservation Office, Indonesia. In our VR system, the following three data sources are integrated to form a 3D point cloud: (1) a 3D point cloud of the overall shape of the temple acquired by photogrammetry using a camera carried by a UAV, (2) a 3D point cloud obtained from precise photogrammetric measurements of selected parts of the temple building, and (3) 3D data of the hidden relief panels recovered from the archived 2D monocular photos using deep learning. Our VR system supports both the first-person view and the bird’s eye view. The first-person view allows immersive observation and appreciation of the cultural heritage. The bird’s eye view is useful for understanding the whole picture. A user can easily switch between the two views by using a user-friendly VR user interface constructed by a 3D game engine.

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 Comparison of Depth Camera and Terrestrial Laser Scanner in Monitoring Structural Deflections

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 201
Author(s):  
Michael Bekele Maru ◽  
Donghwan Lee ◽  
Kassahun Demissie Tola ◽  
Seunghee Park
Keyword(s):  
Optical Sensors ◽  
Point Cloud ◽  
Three Dimensional ◽  
Laser Scanner ◽  
Point Clouds ◽  
Depth Camera ◽  
Terrestrial Laser Scanner ◽  
Cloud Data ◽  
3D Point Clouds ◽  
3D Information

Modeling a structure in the virtual world using three-dimensional (3D) information enhances our understanding, while also aiding in the visualization, of how a structure reacts to any disturbance. Generally, 3D point clouds are used for determining structural behavioral changes. Light detection and ranging (LiDAR) is one of the crucial ways by which a 3D point cloud dataset can be generated. Additionally, 3D cameras are commonly used to develop a point cloud containing many points on the external surface of an object around it. The main objective of this study was to compare the performance of optical sensors, namely a depth camera (DC) and terrestrial laser scanner (TLS) in estimating structural deflection. We also utilized bilateral filtering techniques, which are commonly used in image processing, on the point cloud data for enhancing their accuracy and increasing the application prospects of these sensors in structure health monitoring. The results from these sensors were validated by comparing them with the outputs from a linear variable differential transformer sensor, which was mounted on the beam during an indoor experiment. The results showed that the datasets obtained from both the sensors were acceptable for nominal deflections of 3 mm and above because the error range was less than ±10%. However, the result obtained from the TLS were better than those obtained from the DC.

scholarly journals Fast and Robust Flight Altitude Estimation of Multirotor UAVs in Dynamic Unstructured Environments Using 3D Point Cloud Sensors

Aerospace ◽  
2018 ◽  
Vol 5 (3) ◽  
pp. 94 ◽  
Author(s):  
Hriday Bavle ◽  
Jose Sanchez-Lopez ◽  
Paloma Puente ◽  
Alejandro Rodriguez-Ramos ◽  
Carlos Sampedro ◽  
...  
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Estimation Algorithm ◽  
Indoor Environments ◽  
3D Point Cloud ◽  
Flight Altitude ◽  
Cloud Data ◽  
3D Point Clouds ◽  
Laser Altimeters ◽  
Altitude Estimation

This paper presents a fast and robust approach for estimating the flight altitude of multirotor Unmanned Aerial Vehicles (UAVs) using 3D point cloud sensors in cluttered, unstructured, and dynamic indoor environments. The objective is to present a flight altitude estimation algorithm, replacing the conventional sensors such as laser altimeters, barometers, or accelerometers, which have several limitations when used individually. Our proposed algorithm includes two stages: in the first stage, a fast clustering of the measured 3D point cloud data is performed, along with the segmentation of the clustered data into horizontal planes. In the second stage, these segmented horizontal planes are mapped based on the vertical distance with respect to the point cloud sensor frame of reference, in order to provide a robust flight altitude estimation even in presence of several static as well as dynamic ground obstacles. We validate our approach using the IROS 2011 Kinect dataset available in the literature, estimating the altitude of the RGB-D camera using the provided 3D point clouds. We further validate our approach using a point cloud sensor on board a UAV, by means of several autonomous real flights, closing its altitude control loop using the flight altitude estimated by our proposed method, in presence of several different static as well as dynamic ground obstacles. In addition, the implementation of our approach has been integrated in our open-source software framework for aerial robotics called Aerostack.

scholarly journals GEOMETRIC FEATURES AND THEIR RELEVANCE FOR 3D POINT CLOUD CLASSIFICATION

2017 ◽  
Vol IV-1/W1 ◽  
pp. 157-164 ◽  
Author(s):  
M. Weinmann ◽  
B. Jutzi ◽  
C. Mallet ◽  
M. Weinmann
Keyword(s):  
Influencing Factors ◽  
Point Cloud ◽  
Geometric Features ◽  
3D Point Cloud ◽  
Point Sampling ◽  
Cloud Data ◽  
Semantic Classes ◽  
Automatic Interpretation ◽  
Point Cloud Classification ◽  
The Impact

In this paper, we focus on the automatic interpretation of 3D point cloud data in terms of associating a class label to each 3D point. While much effort has recently been spent on this research topic, little attention has been paid to the influencing factors that affect the quality of the derived classification results. For this reason, we investigate fundamental influencing factors making geometric features more or less relevant with respect to the classification task. We present a framework which consists of five components addressing point sampling, neighborhood recovery, feature extraction, classification and feature relevance assessment. To analyze the impact of the main influencing factors which are represented by the given point sampling and the selected neighborhood type, we present the results derived with different configurations of our framework for a commonly used benchmark dataset for which a reference labeling with respect to three structural classes (<i>linear structures, planar structures</i> and <i>volumetric structures</i>) as well as a reference labeling with respect to five semantic classes (<i>Wire, Pole/Trunk, Façade, Ground</i> and <i>Vegetation</i>) is available.

scholarly journals Multi-view attention-convolution pooling network for 3D point cloud classification

2021 ◽  
Author(s):  
Wenju Wang ◽  
Tao Wang ◽  
Yu Cai
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Information Loss ◽  
Feature Representation ◽  
3D Point Cloud ◽  
Current Output ◽  
3D Point Clouds ◽  
Cloud Classification ◽  
Feature Information ◽  
Point Cloud Classification

AbstractClassifying 3D point clouds is an important and challenging task in computer vision. Currently, classification methods using multiple views lose characteristic or detail information during the representation or processing of views. For this reason, we propose a multi-view attention-convolution pooling network framework for 3D point cloud classification tasks. This framework uses Res2Net to extract the features from multiple 2D views. Our attention-convolution pooling method finds more useful information in the input data related to the current output, effectively solving the problem of feature information loss caused by feature representation and the detail information loss during dimensionality reduction. Finally, we obtain the probability distribution of the model to be classified using a full connection layer and the softmax function. The experimental results show that our framework achieves higher classification accuracy and better performance than other contemporary methods using the ModelNet40 dataset.

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