scholarly journals IMAGE CAPTURE WITH SYNCHRONIZED MULTIPLE-CAMERAS FOR EXTRACTION OF ACCURATE GEOMETRIES

2016 ◽  
Vol XLI-B1 ◽  
pp. 653-660
Author(s):  
M. Koehl ◽  
T. Delacourt ◽  
C. Boutry
Keyword(s):  
Accuracy Assessment ◽  
Low Cost ◽  
Point Clouds ◽  
Image Capture ◽  
Multiple Sensors ◽  
Internal Parameters ◽  
Dense Point ◽  
Set Up ◽  
Major Factors ◽  
Fisheye Lenses

This paper presents a project of recording and modelling tunnels, traffic circles and roads from multiple sensors. The aim is the representation and the accurate 3D modelling of a selection of road infrastructures as dense point clouds in order to extract profiles and metrics from it. Indeed, these models will be used for the sizing of infrastructures in order to simulate exceptional convoy truck routes. The objective is to extract directly from the point clouds the heights, widths and lengths of bridges and tunnels, the diameter of gyrating and to highlight potential obstacles for a convoy. Light, mobile and fast acquisition approaches based on images and videos from a set of synchronized sensors have been tested in order to obtain useable point clouds. The presented solution is based on a combination of multiple low-cost cameras designed on an on-boarded device allowing dynamic captures. The experimental device containing <i>GoPro Hero4</i> cameras has been set up and used for tests in static or mobile acquisitions. That way, various configurations have been tested by using multiple synchronized cameras. These configurations are discussed in order to highlight the best operational configuration according to the shape of the acquired objects. As the precise calibration of each sensor and its optics are major factors in the process of creation of accurate dense point clouds, and in order to reach the best quality available from such cameras, the estimation of the internal parameters of fisheye lenses of the cameras has been processed. Reference measures were also realized by using a 3D TLS (<i>Faro Focus 3D</i>) to allow the accuracy assessment.

scholarly journals IMAGE CAPTURE WITH SYNCHRONIZED MULTIPLE-CAMERAS FOR EXTRACTION OF ACCURATE GEOMETRIES

2016 ◽  
Vol XLI-B1 ◽  
pp. 653-660
Author(s):  
M. Koehl ◽  
T. Delacourt ◽  
C. Boutry
Keyword(s):  
Accuracy Assessment ◽  
Low Cost ◽  
Point Clouds ◽  
Image Capture ◽  
Multiple Sensors ◽  
Internal Parameters ◽  
Dense Point ◽  
Set Up ◽  
Major Factors ◽  
Fisheye Lenses

This paper presents a project of recording and modelling tunnels, traffic circles and roads from multiple sensors. The aim is the representation and the accurate 3D modelling of a selection of road infrastructures as dense point clouds in order to extract profiles and metrics from it. Indeed, these models will be used for the sizing of infrastructures in order to simulate exceptional convoy truck routes. The objective is to extract directly from the point clouds the heights, widths and lengths of bridges and tunnels, the diameter of gyrating and to highlight potential obstacles for a convoy. Light, mobile and fast acquisition approaches based on images and videos from a set of synchronized sensors have been tested in order to obtain useable point clouds. The presented solution is based on a combination of multiple low-cost cameras designed on an on-boarded device allowing dynamic captures. The experimental device containing <i>GoPro Hero4</i> cameras has been set up and used for tests in static or mobile acquisitions. That way, various configurations have been tested by using multiple synchronized cameras. These configurations are discussed in order to highlight the best operational configuration according to the shape of the acquired objects. As the precise calibration of each sensor and its optics are major factors in the process of creation of accurate dense point clouds, and in order to reach the best quality available from such cameras, the estimation of the internal parameters of fisheye lenses of the cameras has been processed. Reference measures were also realized by using a 3D TLS (<i>Faro Focus 3D</i>) to allow the accuracy assessment.

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 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 KINECT V2 AND RGB STEREO CAMERAS INTEGRATION FOR DEPTH MAP ENHANCEMENT

2016 ◽  
Vol XLI-B5 ◽  
pp. 699-702 ◽  
Author(s):  
R. Ravanelli ◽  
A. Nascetti ◽  
M. Crespi
Keyword(s):  
Low Cost ◽  
Depth Map ◽  
Point Clouds ◽  
Quality Standard ◽  
Frame Rate ◽  
Stereo Pair ◽  
Kinect V2 ◽  
Dense Point ◽  
3D Scanners ◽  
Modelling Techniques

Today range cameras are widespread low-cost sensors based on two different principles of operation: we can distinguish between Structured Light (SL) range cameras (Kinect v1, Structure Sensor, ...) and Time Of Flight (ToF) range cameras (Kinect v2, ...). Both the types are easy to use 3D scanners, able to reconstruct dense point clouds at high frame rate. However the depth maps obtained are often noisy and not enough accurate, therefore it is generally essential to improve their quality. Standard RGB cameras can be a valuable solution to solve such issue. The aim of this paper is therefore to evaluate the integration feasibility of these two different 3D modelling techniques, characterized by complementary features and based on standard low-cost sensors. <br><br> For this purpose, a 3D model of a DUPLO<sup>TM</sup> bricks construction was reconstructed both with the Kinect v2 range camera and by processing one stereo pair acquired with a Canon Eos 1200D DSLR camera. The scale of the photgrammetric model was retrieved from the coordinates measured by Kinect v2. The preliminary results are encouraging and show that the foreseen integration could lead to an higher metric accuracy and a major level of completeness with respect to that obtained by using only separated techniques.

scholarly journals Accuracy assessment of building point clouds automatically generated from iphone images

2014 ◽  
Vol XL-5 ◽  
pp. 547-552 ◽  
Author(s):  
B. Sirmacek ◽  
R. Lindenbergh
Keyword(s):  
Measurement Errors ◽  
Point Cloud ◽  
Model Updating ◽  
Accuracy Assessment ◽  
Low Cost ◽  
Point Clouds ◽  
3D Models ◽  
Map Updating ◽  
Cloud Points ◽  
Point To Point

Low-cost sensor generated 3D models can be useful for quick 3D urban model updating, yet the quality of the models is questionable. In this article, we evaluate the reliability of an automatic point cloud generation method using multi-view iPhone images or an iPhone video file as an input. We register such automatically generated point cloud on a TLS point cloud of the same object to discuss accuracy, advantages and limitations of the iPhone generated point clouds. For the chosen example showcase, we have classified 1.23% of the iPhone point cloud points as outliers, and calculated the mean of the point to point distances to the TLS point cloud as 0.11 m. Since a TLS point cloud might also include measurement errors and noise, we computed local noise values for the point clouds from both sources. Mean (μ) and standard deviation (&amp;sigma;) of roughness histograms are calculated as (μ<sub>1</sub> = 0.44 m., &amp;sigma;<sub>1</sub> = 0.071 m.) and (μ<sub>2</sub> = 0.025 m., &amp;sigma;<sub>2</sub> = 0.037 m.) for the iPhone and TLS point clouds respectively. Our experimental results indicate possible usage of the proposed automatic 3D model generation framework for 3D urban map updating, fusion and detail enhancing, quick and real-time change detection purposes. However, further insights should be obtained first on the circumstances that are needed to guarantee a successful point cloud generation from smartphone images.

scholarly journals COMBINATION OF TLS POINT CLOUDS AND 3D DATA FROM KINECT V2 SENSOR TO COMPLETE INDOOR MODELS

2016 ◽  
Vol XLI-B5 ◽  
pp. 659-666 ◽  
Author(s):  
E. Lachat ◽  
T. Landes ◽  
P. Grussenmeyer
Keyword(s):  
Point Cloud ◽  
Low Cost ◽  
Measurement Techniques ◽  
Laser Scanner ◽  
Point Clouds ◽  
Final Model ◽  
Kinect V2 ◽  
Multiple Sensors ◽  
3D Data ◽  
Final Data

The combination of data coming from multiple sensors is more and more applied for remote sensing issues (multi-sensor imagery) but also in cultural heritage or robotics, since it often results in increased robustness and accuracy of the final data. In this paper, the reconstruction of building elements such as window frames or door jambs scanned thanks to a low cost 3D sensor (Kinect v2) is presented. Their combination within a global point cloud of an indoor scene acquired with a terrestrial laser scanner (TLS) is considered. If the added elements acquired with the Kinect sensor enable to reach a better level of detail of the final model, an adapted acquisition protocol may also provide several benefits as for example time gain. The paper aims at analyzing whether the two measurement techniques can be complementary in this context. The limitations encountered during the acquisition and reconstruction steps are also investigated.

scholarly journals Terrestrial Image-Based Point Clouds for Mapping Near-Ground Vegetation Structure: Potential and Limitations

Fire ◽  
2020 ◽  
Vol 3 (4) ◽  
pp. 59
Author(s):  
Luke Wallace ◽  
Bryan Hally ◽  
Samuel Hillman ◽  
Simon D. Jones ◽  
Karin Reinke
Keyword(s):  
Low Cost ◽  
Rapid Assessment ◽  
3D Structure ◽  
Point Clouds ◽  
Quantitative Information ◽  
Ground Vegetation ◽  
Specific Information ◽  
Image Capture ◽  
Near Surface ◽  
Management Interventions

Site-specific information concerning fuel hazard characteristics is needed to support wildfire management interventions and fuel hazard reduction programs. Currently, routine visual assessments provide subjective information, with the resulting estimate of fuel hazard varying due to observer experience and the rigor applied in making assessments. Terrestrial remote sensing techniques have been demonstrated to be capable of capturing quantitative information on the spatial distribution of biomass to inform fuel hazard assessments. This paper explores the use of image-based point clouds generated from imagery captured using a low-cost compact camera for describing the fuel hazard within the surface and near-surface layers. Terrestrial imagery was obtained at three distances for five target plots. Subsets of these images were then processed to determine the effect of varying overlap and distribution of image captures. The majority of the point clouds produced using this image-based technique provide an accurate representation of the 3D structure of the surface and near-surface fuels. Results indicate that high image overlap and pixel size are critical; multi-angle image capture is shown to be crucial in providing a representation of the vertical stratification of fuel. Terrestrial image-based point clouds represent a viable technique for low cost and rapid assessment of fuel structure.

scholarly journals A method based on structure-from-motion photogrammetry to generate sub-millimetre-resolution digital elevation models for investigating rock breakdown features

2019 ◽  
Vol 7 (1) ◽  
pp. 45-66 ◽  
Author(s):  
Ankit Kumar Verma ◽  
Mary Carol Bourke
Keyword(s):  
High Resolution ◽  
Structure From Motion ◽  
Low Cost ◽  
Digital Elevation Models ◽  
Point Clouds ◽  
Experimental Conditions ◽  
Weathered Rock ◽  
Topographic Data ◽  
Digital Elevation ◽  
Dense Point

Abstract. We have generated sub-millimetre-resolution DEMs of weathered rock surfaces using SfM photogrammetry techniques. We apply a close-range method based on structure-from-motion (SfM) photogrammetry in the field and use it to generate high-resolution topographic data for weathered boulders and bedrock. The method was pilot tested on extensively weathered Triassic Moenkopi sandstone outcrops near Meteor Crater in Arizona. Images were taken in the field using a consumer-grade DSLR camera and were processed in commercially available software to build dense point clouds. The point clouds were registered to a local 3-D coordinate system (x, y, z), which was developed using a specially designed triangle-coded control target and then exported as digital elevation models (DEMs). The accuracy of the DEMs was validated under controlled experimental conditions. A number of checkpoints were used to calculate errors. We also evaluated the effects of image and camera parameters on the accuracy of our DEMs. We report a horizontal error of 0.5 mm and vertical error of 0.3 mm in our experiments. Our approach provides a low-cost method for obtaining very high-resolution topographic data on weathered rock surfaces (area < 10 m2). The results from our case study confirm the efficacy of the method at this scale and show that the data acquisition equipment is sufficiently robust and portable. This is particularly important for field conditions in remote locations or steep terrain where portable and efficient methods are required.

Alternative Methods for Developing and Assessing the Accuracy of UAV-Derived DEMs

2019 ◽  
pp. 1175-1196
Author(s):  
Dion J. Wiseman ◽  
Jurjen van der Sluijs
Keyword(s):  
High Resolution ◽  
Accuracy Assessment ◽  
Point Clouds ◽  
Alternative Methods ◽  
Data Sets ◽  
Digital Terrain ◽  
Terrain Models ◽  
Dense Point ◽  
Data Points ◽  
Derived Data

Digital terrain models are invaluable datasets that are frequently used for visualizing, modeling, and analyzing Earth surface processes. Accurate models covering local scale landscape features are often very expensive and have poor temporal resolution. This research investigates the utility of UAV acquired imagery for generating high resolution terrain models and provides a detailed accuracy assessment according to recommended protocols. High resolution UAV imagery was acquired over a localized dune complex in southwestern Manitoba, Canada and two alternative workflows were evaluated for extracting point clouds. UAV-derived data points were then compared to reference data sets acquired using mapping grade GPS receivers and a total station. Results indicated that the UAV imagery was capable of producing dense point clouds and high resolution terrain models with mean errors as low as -0.15 m and RMSE values of 0.42 m depending on the resolution of the image dataset and workflow employed.

scholarly journals GENERATING ACCURATE 3D MODELS OF ARCHITECTURAL HERITAGE STRUCTURES USING LOW-COST CAMERA AND OPEN SOURCE ALGORITHMS

2017 ◽  
Vol XLII-5/W1 ◽  
pp. 99-104
Author(s):  
M. Zacharek ◽  
P. Delis ◽  
M. Kedzierski ◽  
A. Fryskowska
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Web Application ◽  
Low Cost ◽  
Digital Camera ◽  
Point Clouds ◽  
3D Models ◽  
Dense Image ◽  
Dense Point ◽  
Historical Heritage

These studies have been conductedusing non-metric digital camera and dense image matching algorithms, as non-contact methods of creating monuments documentation.In order toprocess the imagery, few open-source software and algorithms of generating adense point cloud from images have been executed. In the research, the OSM Bundler, VisualSFM software, and web application ARC3D were used. Images obtained for each of the investigated objects were processed using those applications, and then dense point clouds and textured 3D models were created. As a result of post-processing, obtained models were filtered and scaled.The research showedthat even using the open-source software it is possible toobtain accurate 3D models of structures (with an accuracy of a few centimeters), but for the purpose of documentation and conservation of cultural and historical heritage, such accuracy can be insufficient.

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