scholarly journals ACCURACY ASSESSMENT OF UNDERWATER PHOTOGRAMMETRIC THREE DIMENSIONAL MODELLING FOR CORAL REEFS

2016 ◽  
Vol XLI-B5 ◽  
pp. 821-828 ◽  
Author(s):  
T. Guo ◽  
A. Capra ◽  
M. Troyer ◽  
A. Gruen ◽  
A. J. Brooks ◽  
...  
Keyword(s):  
Coral Cover ◽  
Accuracy Assessment ◽  
Low Cost ◽  
Three Dimensional ◽  
Point Clouds ◽  
3D Modelling ◽  
Relative Accuracy ◽  
3D Point Clouds ◽  
The Individual ◽  
Working Distance

Recent advances in automation of photogrammetric 3D modelling software packages have stimulated interest in reconstructing highly accurate 3D object geometry in unconventional environments such as underwater utilizing simple and low-cost camera systems. The accuracy of underwater 3D modelling is affected by more parameters than in single media cases. This study is part of a larger project on 3D measurements of temporal change of coral cover in tropical waters. It compares the accuracies of 3D point clouds generated by using images acquired from a system camera mounted in an underwater housing and the popular GoPro cameras respectively. A precisely measured calibration frame was placed in the target scene in order to provide accurate control information and also quantify the errors of the modelling procedure. In addition, several objects (cinder blocks) with various shapes were arranged in the air and underwater and 3D point clouds were generated by automated image matching. These were further used to examine the relative accuracy of the point cloud generation by comparing the point clouds of the individual objects with the objects measured by the system camera in air (the best possible values). Given a working distance of about 1.5 m, the GoPro camera can achieve a relative accuracy of 1.3 mm in air and 2.0 mm in water. The system camera achieved an accuracy of 1.8 mm in water, which meets our requirements for coral measurement in this system.

scholarly journals ACCURACY ASSESSMENT OF UNDERWATER PHOTOGRAMMETRIC THREE DIMENSIONAL MODELLING FOR CORAL REEFS

2016 ◽  
Vol XLI-B5 ◽  
pp. 821-828 ◽  
Author(s):  
T. Guo ◽  
A. Capra ◽  
M. Troyer ◽  
A. Gruen ◽  
A. J. Brooks ◽  
...  
Keyword(s):  
Coral Cover ◽  
Accuracy Assessment ◽  
Low Cost ◽  
Three Dimensional ◽  
Point Clouds ◽  
3D Modelling ◽  
Relative Accuracy ◽  
3D Point Clouds ◽  
The Individual ◽  
Working Distance

Recent advances in automation of photogrammetric 3D modelling software packages have stimulated interest in reconstructing highly accurate 3D object geometry in unconventional environments such as underwater utilizing simple and low-cost camera systems. The accuracy of underwater 3D modelling is affected by more parameters than in single media cases. This study is part of a larger project on 3D measurements of temporal change of coral cover in tropical waters. It compares the accuracies of 3D point clouds generated by using images acquired from a system camera mounted in an underwater housing and the popular GoPro cameras respectively. A precisely measured calibration frame was placed in the target scene in order to provide accurate control information and also quantify the errors of the modelling procedure. In addition, several objects (cinder blocks) with various shapes were arranged in the air and underwater and 3D point clouds were generated by automated image matching. These were further used to examine the relative accuracy of the point cloud generation by comparing the point clouds of the individual objects with the objects measured by the system camera in air (the best possible values). Given a working distance of about 1.5 m, the GoPro camera can achieve a relative accuracy of 1.3 mm in air and 2.0 mm in water. The system camera achieved an accuracy of 1.8 mm in water, which meets our requirements for coral measurement in this system.

scholarly journals Estimating tree stem diameters and volume from smartphone photogrammetric point clouds

2019 ◽  
Vol 93 (3) ◽  
pp. 411-429 ◽  
Author(s):  
Maria Immacolata Marzulli ◽  
Pasi Raumonen ◽  
Roberto Greco ◽  
Manuela Persia ◽  
Patrizia Tartarino
Keyword(s):  
Low Cost ◽  
Three Dimensional ◽  
Laser Scanner ◽  
Point Clouds ◽  
Stem Volume ◽  
Forest Plot ◽  
3D Point Clouds ◽  
Dense Point ◽  
Minimum Number ◽  
Scale Point

Abstract Methods for the three-dimensional (3D) reconstruction of forest trees have been suggested for data from active and passive sensors. Laser scanner technologies have become popular in the last few years, despite their high costs. Since the improvements in photogrammetric algorithms (e.g. structure from motion—SfM), photographs have become a new low-cost source of 3D point clouds. In this study, we use images captured by a smartphone camera to calculate dense point clouds of a forest plot using SfM. Eighteen point clouds were produced by changing the densification parameters (Image scale, Point density, Minimum number of matches) in order to investigate their influence on the quality of the point clouds produced. In order to estimate diameter at breast height (d.b.h.) and stem volumes, we developed an automatic method that extracts the stems from the point cloud and then models them with cylinders. The results show that Image scale is the most influential parameter in terms of identifying and extracting trees from the point clouds. The best performance with cylinder modelling from point clouds compared to field data had an RMSE of 1.9 cm and 0.094 m3, for d.b.h. and volume, respectively. Thus, for forest management and planning purposes, it is possible to use our photogrammetric and modelling methods to measure d.b.h., stem volume and possibly other forest inventory metrics, rapidly and without felling trees. The proposed methodology significantly reduces working time in the field, using ‘non-professional’ instruments and automating estimates of dendrometric parameters.

scholarly journals A Self-Assembly Portable Mobile Mapping System for Archeological Reconstruction Based on VSLAM-Photogrammetric Algorithm

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3952 ◽  
Author(s):  
* ◽  
*
Keyword(s):  
Self Assembly ◽  
Average Distance ◽  
Low Cost ◽  
Three Dimensional ◽  
Point Clouds ◽  
3D Models ◽  
Data Capture ◽  
Information Modeling ◽  
3D Point Clouds ◽  
Mean Square Errors

Three Dimensional (3D) models are widely used in clinical applications, geosciences, cultural heritage preservation, and engineering; this, together with new emerging needs such as building information modeling (BIM) develop new data capture techniques and devices with a low cost and reduced learning curve that allow for non-specialized users to employ it. This paper presents a simple, self-assembly device for 3D point clouds data capture with an estimated base price under €2500; furthermore, a workflow for the calculations is described that includes a Visual SLAM-photogrammetric threaded algorithm that has been implemented in C++. Another purpose of this work is to validate the proposed system in BIM working environments. To achieve it, in outdoor tests, several 3D point clouds were obtained and the coordinates of 40 points were obtained by means of this device, with data capture distances ranging between 5 to 20 m. Subsequently, those were compared to the coordinates of the same targets measured by a total station. The Euclidean average distance errors and root mean square errors (RMSEs) ranging between 12–46 mm and 8–33 mm respectively, depending on the data capture distance (5–20 m). Furthermore, the proposed system was compared with a commonly used photogrammetric methodology based on Agisoft Metashape software. The results obtained demonstrate that the proposed system satisfies (in each case) the tolerances of ‘level 1’ (51 mm) and ‘level 2’ (13 mm) for point cloud acquisition in urban design and historic documentation, according to the BIM Guide for 3D Imaging (U.S. General Services).

scholarly journals Improvement of 3D Power Line Extraction from Multiple Low-Cost UAV Imagery Using Wavelet Analysis

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 700 ◽  
Author(s):  
Anna Fryskowska
Keyword(s):  
Point Cloud ◽  
Accuracy Assessment ◽  
Low Cost ◽  
Three Dimensional ◽  
Point Clouds ◽  
Power Line ◽  
Power Lines ◽  
Remotely Sensed Data ◽  
Processing Algorithms

Three-dimensional (3D) mapping of power lines is very important for power line inspection. Many remotely-sensed data products like light detection and ranging (LiDAR) have been already studied for power line surveys. More and more data are being obtained via photogrammetric measurements. This increases the need for the implementation of advanced processing techniques. In recent years, there have been several developments in visualisation techniques using UAV (unmanned aerial vehicle) platform photography. The most modern of such imaging systems have the ability to generate dense point clouds. However, image-based point cloud accuracy is very often various (unstable) and dependent on the radiometric quality of images and the efficiency of image processing algorithms. The main factor influencing the point cloud quality is noise. Such problems usually arise with data obtained via low-cost UAV platforms. Therefore, generated point clouds representing power lines are usually incomplete and noisy. To obtain a complete and accurate 3D model of power lines and towers, it is necessary to develop improved data processing algorithms. The experiment tested the algorithms on power lines with different voltages. This paper presents the wavelet-based method of processing data acquired with a low-cost UAV camera. The proposed, original method involves the application of algorithms for coarse filtration and precise filtering. In addition, a new way of calculating the recommended flight height was proposed. At the end, the accuracy assessment of this two-stage filtration process was examined. For this, point quality indices were proposed. The experimental results show that the proposed algorithm improves the quality of low-cost point clouds. The proposed methods improve the accuracy of determining the parameters of the lines by more than twice. About 10% of noise is reduced by using the wavelet-based approach.

scholarly journals Automatic Reconstruction of Building-Scale Indoor 3D Environment with a Deep-Reinforcement-Learning-Based Mobile Robot

2021 ◽  
Vol 6 ◽  
pp. 11-23
Author(s):  
Menglong Yang ◽  
Katashi Nagao
Keyword(s):  
Reinforcement Learning ◽  
Mobile Robot ◽  
Real World ◽  
Low Cost ◽  
Three Dimensional ◽  
Point Clouds ◽  
Autonomous Agent ◽  
Entire Area ◽  
The Real ◽  
3D Point Clouds

The aim of this paper is to digitize the environments in which humans live, at low cost, and reconstruct highly accurate three-dimensional environments that are based on those in the real world. This three-dimensional content can be used such as for virtual reality environments and three-dimensional maps for automatic driving systems. In general, however, a three-dimensional environment must be carefully reconstructed by manually moving the sensors used to first scan the real environment on which the three-dimensional one is based. This is done so that every corner of an entire area can be measured, but time and costs increase as the area expands. Therefore, a system that creates three-dimensional content that is based on real-world large-scale buildings at low cost is proposed. This involves automatically scanning the indoors with a mobile robot that uses low-cost sensors and generating 3D point clouds. When the robot reaches an appropriate measurement position, it collects the three-dimensional data of shapes observable from that position by using a 3D sensor and 360-degree panoramic camera. The problem of determining an appropriate measurement position is called the “next best view problem,” and it is difficult to solve in a complicated indoor environment. To deal with this problem, a deep reinforcement learning method is employed. It combines reinforcement learning, with which an autonomous agent learns strategies for selecting behavior, and deep learning done using a neural network. As a result, 3D point cloud data can be generated with better quality than the conventional rule-based approach.

scholarly journals Accuracy Assessment of 3D Model Based on Laser Scan and Photogrammetry Data

2022 ◽  
pp. 4195-4207
Author(s):  
Marwa Mohamed ◽  
Zahra Ezz El Din ◽  
Laila Qais
Keyword(s):  
Laser Scanning ◽  
3D Model ◽  
Accuracy Assessment ◽  
Three Dimensional ◽  
Point Clouds ◽  
Field Work ◽  
Reference Points ◽  
3D Point Clouds ◽  
The Difference ◽  
The University

    A three-dimensional (3D) model extraction represents the best way to reflect the reality in all details. This explains the trends and tendency of many scientific disciplines towards making measurements, calculations and monitoring in various fields using such model. Although there are many ways to produce the 3D model like as images, integration techniques, and laser scanning, however, the quality of their products is not the same in terms of accuracy and detail. This article aims to assess the 3D point clouds model accuracy results from close range images and laser scan data based on Agi soft photoscan and cloud compare software to determine the compatibility of both datasets for several applications. College of Science, Departments of Mathematics and Computer in the University of Baghdad campus were exploited to create the proposed 3D model as this area location, which is one of the distinctive features of the university, allows making measurements freely from all sides. Results of this study supported by statistical analysis including 2 sample T-test and RMSE calculation in addition to visual comparison. Through this research, we note that the laser3D model provides many points in a short time, so it will reduce the field work and also its data is faster in processing to produce a reliable model of the scanned area compared with data derived from photogrammetry, then the difference were computed for all the reference points.

scholarly journals Structure from motion (SfM) photogrammetry as alternative to laser scanning for 3D modelling of historical monuments

2020 ◽  
Vol 5 (2) ◽  
Author(s):  
Omar Al Khalil
Keyword(s):  
Structure From Motion ◽  
Laser Scanning ◽  
Low Cost ◽  
Laser Scanner ◽  
Digital Camera ◽  
Point Clouds ◽  
3D Modelling ◽  
3D Point Clouds ◽  
Historical Monuments ◽  
Culture Heritage

During the past few years, new developments have occurred in the field of 3D photogrammetric modeling of culture heritage. One of these developments is the expansion of 3D photogrammetric modeling open-source software, such as VisualSfM, and cost-effective licensed software, such as Agisoft Metashape into the practical and affordable world. This type of SfM (Structure from Motion) software offers the world of 3D modelling of culture heritage a powerful tool for documentation and visualization. On the other hand, low-cost cameras are now available on the market. These cameras are characterized by high resolution and good quality lens, which makes them suitable for photogrammetric modelling. This paper reports on the results of the application of a SfM photogrammetry system in the 3D modelling of Safita Tower, a medieval structure in Safita, north-western Syria. The applied photogrammetric system consists of the Nikon Coolpix P100 10 MP digital camera and the commercial software Agisoft Metashape. The resulted 3D point clouds were compared with an available dense point cloud acquired by a laser scanner. This comparison proved that the low-cost SfM photogrammetry is an accurate methodology to 3D modeling historical monuments. 

scholarly journals Enhancing Image-Based Multiscale Heritage Recording with Near-Infrared Data

2020 ◽  
Vol 9 (4) ◽  
pp. 269 ◽  
Author(s):  
Efstathios Adamopoulos ◽  
Fulvio Rinaudo
Keyword(s):  
Near Infrared ◽  
Low Cost ◽  
Three Dimensional ◽  
Point Clouds ◽  
Archaeological Site ◽  
Short Review ◽  
Additional Information ◽  
3D Point Clouds ◽  
Heritage Science ◽  
Imaging Sensors

Passive sensors, operating in the visible (VIS) spectrum, have widely been used towards the trans-disciplinary documentation, understanding, and protection of tangible cultural heritage (CH). Although, many heritage science fields benefit significantly from additional information that can be acquired in the near-infrared (NIR) spectrum. NIR imagery, captured for heritage applications, has been mostly investigated with two-dimensional (2D) approaches or by 2D-to-three-dimensional (3D) integrations following complicated techniques, including expensive imaging sensors and setups. The availability of high-resolution digital modified cameras and software implementations of Structure-from-Motion (SfM) and Multiple-View-Stereo (MVS) algorithms, has made the production of models with spectral textures more feasible than ever. In this research, a short review of image-based 3D modeling with NIR data is attempted. The authors aim to investigate the use of near-infrared imagery from relatively low-cost modified sensors for heritage digitization, alongside the usefulness of spectral textures produced, oriented towards heritage science. Therefore, thorough experimentation and assessment with different software are conducted and presented, utilizing NIR imagery and SfM/MVS methods. Dense 3D point clouds and textured meshes have been produced and evaluated for their metric validity and radiometric quality, comparing to results produced from VIS imagery. The datasets employed come from heritage assets of different dimensions, from an archaeological site to a medium-sized artwork, to evaluate implementation on different levels of accuracy and specifications of texture resolution.

scholarly journals Monitoring Selective Logging in a Pine-Dominated Forest in Central Germany with Repeated Drone Flights Utilizing A Low Cost RTK Quadcopter

Drones ◽  
2020 ◽  
Vol 4 (2) ◽  
pp. 11 ◽  
Author(s):  
Christian Thiel ◽  
Marlin M. Müller ◽  
Christian Berger ◽  
Felix Cremer ◽  
Clémence Dubois ◽  
...  
Keyword(s):  
Low Cost ◽  
Three Dimensional ◽  
Point Clouds ◽  
Selective Logging ◽  
Ease Of Use ◽  
Tree Level ◽  
Unmanned Aerial Systems ◽  
3D Point Clouds ◽  
Individual Trees ◽  
New Generation

There is no doubt that unmanned aerial systems (UAS) will play an increasing role in Earth observation in the near future. The field of application is very broad and includes aspects of environmental monitoring, security, humanitarian aid, or engineering. In particular, drones with camera systems are already widely used. The capability to compute ultra-high-resolution orthomosaics and three-dimensional (3D) point clouds from UAS imagery generates a wide interest in such systems, not only in the science community, but also in industry and agencies. In particular, forestry sciences benefit from ultra-high-structural and spectral information as regular tree level-based monitoring becomes feasible. There is a great need for this kind of information as, for example, due to the spring and summer droughts in Europe in the years 2018/2019, large quantities of individual trees were damaged or even died. This study focuses on selective logging at the level of individual trees using repeated drone flights. Using the new generation of UAS, which allows for sub-decimeter-level positioning accuracies, a change detection approach based on bi-temporal UAS acquisitions was implemented. In comparison to conventional UAS, the effort of implementing repeated drone flights in the field was low, because no ground control points needed to be surveyed. As shown in this study, the geometrical offset between the two collected datasets was below 10 cm across the site, which enabled a direct comparison of both datasets without the need for post-processing (e.g., image matching). For the detection of logged trees, we utilized the spectral and height differences between both acquisitions. For their delineation, an object-based approach was employed, which was proven to be highly accurate (precision = 97.5%; recall = 91.6%). Due to the ease of use of such new generation, off-the-shelf consumer drones, their decreasing purchase costs, the quality of available workflows for data processing, and the convincing results presented here, UAS-based data can and should complement conventional forest inventory practices.

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