scholarly journals DEVELOPMENT OF MULTI-VIEW STEREO CONSIDERING ACCURACY OF EXTERIOR ORIENTATION ELEMENTS

2019 ◽  
Vol XLII-2/W18 ◽  
pp. 47-52
Author(s):  
T. Fuse ◽  
H. Ikezawa
Keyword(s):  
Weighting Function ◽  
Ground Level ◽  
Point Clouds ◽  
Tangent Plane ◽  
Bundle Adjustment ◽  
Exterior Orientation ◽  
Geometric Conditions ◽  
3D Point Clouds ◽  
Aerial Vehicle ◽  
Improved Accuracy

Abstract. Structure from motion (SfM) has been widely used to achieve automatic 3D reconstructions. However, as the 3D point clouds obtained via SfM are sparse, multi-view stereo (MVS) was developed to compensate for this sparseness. The accuracy of the 3D surface depends on the accuracy of the orientation elements based on the SfM. Additionally, in the case of an unmanned aerial vehicle (UAV), SfM exhibits a decrease in the accuracy of the orientation elements during complex camera movements. This paper proposes a patch-based MVS (PMVS) method considering the accuracy of the orientation elements. The proposed method involves applying the global SfM, estimating accuracy of exterior orientation (EO) elements, and introducing the accuracy of EO elements to PMVS. The PMVS approximates an object surface by using small rectangular patches, namely local tangent plane approximation. The patches are optimized by minimizing the sum of the photometric discrepancy scores. The accuracy of the EO elements is introduced to the patch optimization as weighting function. This accuracy is defined using the variances of the estimated parameters in the bundle adjustment. We also investigate the types of weighting functions. The results indicate that the proposed method is capable of considering geometric conditions during patch estimation. The proposed method was applied to the three types of image datasets, i.e., images captured using an SLR camera at ground level, images captured using a UAV equipped with a SLR camera, and images captured using an airplane equipped with an oblique camera. Through the experimental results, the improved accuracy and the effectiveness of the proposed method were confirmed.

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 Estimating Maize Above-Ground Biomass Using 3D Point Clouds of Multi-Source Unmanned Aerial Vehicle Data at Multi-Spatial Scales

2019 ◽  
Vol 11 (22) ◽  
pp. 2678 ◽  
Author(s):  
Zhu ◽  
Sun ◽  
Peng ◽  
Huang ◽  
Li ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Spatial Scales ◽  
Point Clouds ◽  
Support Vector ◽  
Above Ground Biomass ◽  
Ground Biomass ◽  
3D Point Clouds ◽  
Vehicle Data ◽  
Aerial Vehicle ◽  
Multispectral Camera

Crop above-ground biomass (AGB) is a key parameter used for monitoring crop growth and predicting yield in precision agriculture. Estimating the crop AGB at a field scale through the use of unmanned aerial vehicles (UAVs) is promising for agronomic application, but the robustness of the methods used for estimation needs to be balanced with practical application. In this study, three UAV remote sensing flight missions (using a multiSPEC-4C multispectral camera, a Micasense RedEdge-M multispectral camera, and an Alpha Series AL3-32 Light Detection and Ranging (LiDAR) sensor onboard three different UAV platforms) were conducted above three long-term experimental plots with different tillage treatments in 2018. We investigated the performances of the multi-source UAV-based 3D point clouds at multi-spatial scales using the traditional multi-variable linear regression model (OLS), random forest (RF), backpropagation neural network (BP), and support vector machine (SVM) methods for accurate AGB estimation. Results showed that crop height (CH) was a robust proxy for AGB estimation, and that high spatial resolution in CH datasets helps to improve maize AGB estimation. Furthermore, the OLS, RF, BP, and SVM methods all maintained an acceptable accuracy for AGB estimation; however, the SVM and RF methods performed slightly more robustly. This study is expected to optimize UAV systems and algorithms for specific agronomic applications.

scholarly journals APPLICATIONS OF PANORAMIC IMAGES: FROM 720° PANORAMA TO INTERIOR 3D MODELS OF AUGMENTED REALITY

2015 ◽  
Vol XL-4/W5 ◽  
pp. 189-192 ◽  
Author(s):  
I.-C. Lee ◽  
F. Tsai
Keyword(s):  
Augmented Reality ◽  
Focal Length ◽  
Three Dimensional ◽  
Point Clouds ◽  
3D Models ◽  
Exterior Orientation ◽  
3D Point Clouds ◽  
Panoramic Images ◽  
Main Station ◽  
Orientation Parameters

A series of panoramic images are usually used to generate a 720° panorama image. Although panoramic images are typically used for establishing tour guiding systems, in this research, we demonstrate the potential of using panoramic images acquired from multiple sites to create not only 720° panorama, but also three-dimensional (3D) point clouds and 3D indoor models. Since 3D modeling is one of the goals of this research, the location of the panoramic sites needed to be carefully planned in order to maintain a robust result for close-range photogrammetry. After the images are acquired, panoramic images are processed into 720° panoramas, and these panoramas which can be used directly as panorama guiding systems or other applications. <br><br> In addition to these straightforward applications, interior orientation parameters can also be estimated while generating 720° panorama. These parameters are focal length, principle point, and lens radial distortion. The panoramic images can then be processed with closerange photogrammetry procedures to extract the exterior orientation parameters and generate 3D point clouds. In this research, VisaulSFM, a structure from motion software is used to estimate the exterior orientation, and CMVS toolkit is used to generate 3D point clouds. Next, the 3D point clouds are used as references to create building interior models. In this research, Trimble Sketchup was used to build the model, and the 3D point cloud was added to the determining of locations of building objects using plane finding procedure. In the texturing process, the panorama images are used as the data source for creating model textures. This 3D indoor model was used as an Augmented Reality model replacing a guide map or a floor plan commonly used in an on-line touring guide system. <br><br> The 3D indoor model generating procedure has been utilized in two research projects: a cultural heritage site at Kinmen, and Taipei Main Station pedestrian zone guidance and navigation system. The results presented in this paper demonstrate the potential of using panoramic images to generate 3D point clouds and 3D models. However, it is currently a manual and labor-intensive process. A research is being carried out to Increase the degree of automation of these procedures.

An Improved Method for Restoring the Shape of 3D Point Cloud Surfaces

2018 ◽  
Vol 9 (2) ◽  
pp. 37-53
Author(s):  
Sinh Van Nguyen ◽  
Ha Manh Tran ◽  
Minh Khai Tran
Keyword(s):  
Geometric Modeling ◽  
Point Cloud ◽  
Point Clouds ◽  
Tangent Plane ◽  
Surface Patch ◽  
3D Point Cloud ◽  
Improved Method ◽  
Initial Shape ◽  
Cloud Data ◽  
3D Point Clouds

Building 3D objects or reconstructing their surfaces from 3D point cloud data are researched activities in the field of geometric modeling and computer graphics. In the recent years, they are also studied and used in some fields such as: graph models and simulation; image processing or restoration of digital heritages. This article presents an improved method for restoring the shape of 3D point cloud surfaces. The method is a combination of creating a Bezier surface patch and computing tangent plane of 3D points to fill holes on a surface of 3D point clouds. This method is described as follows: at first, a boundary for each hole on the surface is identified. The holes are then filled by computing Bezier curves of surface patches to find missing points. After that, the holes are refined based on two steps (rough and elaborate) to adjust the inserted points and preserve the local curvature of the holes. The contribution of the proposed method has been shown in processing time and the novelty of combined computation in this method has preserved the initial shape of the surface

scholarly journals GNSS/INS-Assisted Structure from Motion Strategies for UAV-Based Imagery over Mechanized Agricultural Fields

2020 ◽  
Vol 12 (3) ◽  
pp. 351 ◽  
Author(s):  
Seyyed Meghdad Hasheminasab ◽  
Tian Zhou ◽  
Ayman Habib
Keyword(s):  
3D Reconstruction ◽  
Structure From Motion ◽  
Point Clouds ◽  
Bundle Adjustment ◽  
Agricultural Fields ◽  
System Calibration ◽  
3D Point Clouds ◽  
Digital Agriculture ◽  
Orientation Parameters ◽  
Calibration Parameters

Acquired imagery by unmanned aerial vehicles (UAVs) has been widely used for three-dimensional (3D) reconstruction/modeling in various digital agriculture applications, such as phenotyping, crop monitoring, and yield prediction. 3D reconstruction from well-textured UAV-based images has matured and the user community has access to several commercial and opensource tools that provide accurate products at a high level of automation. However, in some applications, such as digital agriculture, due to repetitive image patterns, these approaches are not always able to produce reliable/complete products. The main limitation of these techniques is their inability to establish a sufficient number of correctly matched features among overlapping images, causing incomplete and/or inaccurate 3D reconstruction. This paper provides two structure from motion (SfM) strategies, which use trajectory information provided by an onboard survey-grade global navigation satellite system/inertial navigation system (GNSS/INS) and system calibration parameters. The main difference between the proposed strategies is that the first one—denoted as partially GNSS/INS-assisted SfM—implements the four stages of an automated triangulation procedure, namely, imaging matching, relative orientation parameters (ROPs) estimation, exterior orientation parameters (EOPs) recovery, and bundle adjustment (BA). The second strategy— denoted as fully GNSS/INS-assisted SfM—removes the EOPs estimation step while introducing a random sample consensus (RANSAC)-based strategy for removing matching outliers before the BA stage. Both strategies modify the image matching by restricting the search space for conjugate points. They also implement a linear procedure for ROPs’ refinement. Finally, they use the GNSS/INS information in modified collinearity equations for a simpler BA procedure that could be used for refining system calibration parameters. Eight datasets over six agricultural fields are used to evaluate the performance of the developed strategies. In comparison with a traditional SfM framework and Pix4D Mapper Pro, the proposed strategies are able to generate denser and more accurate 3D point clouds as well as orthophotos without any gaps.

scholarly journals ASSESSMENT OF LANDSLIDE-INDUCED DISPLACEMENT AND DEFORMATION OF ABOVE-GROUND OBJECTS USING UAV-BORNE AND AIRBORNE LASER SCANNING DATA

2019 ◽  
Vol IV-2/W5 ◽  
pp. 461-467 ◽  
Author(s):  
T. Zieher ◽  
M. Bremer ◽  
M. Rutzinger ◽  
J. Pfeiffer ◽  
P. Fritzmann ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Laser Scanner ◽  
Point Clouds ◽  
Airborne Laser Scanning ◽  
Additional Information ◽  
3D Point Clouds ◽  
Airborne Laser ◽  
Displacement Vectors ◽  
Multi Temporal ◽  
Aerial Vehicle

<p><strong>Abstract.</strong> Multi-temporal 3D point clouds acquired with a laser scanner can be efficiently used for an area-wide assessment of landslide-induced surface changes. In the present study, displacements of the Vögelsberg landslide (Tyrol, Austria) are assessed based on available data acquired with airborne laser scanning (ALS) in 2013 and data acquired with an unmanned aerial vehicle (UAV) equipped with a laser scanner (ULS) in 2018. Following the data pre-processing steps including registration and ground filtering, buildings are segmented and extracted from the datasets. The roofs, represented as multi-temporal 3D point clouds are then used to derive displacement vectors with a novel matching tool based on the iterative closest point (ICP) algorithm. The resulting mean annual displacements are compared to the results of a geodetic monitoring based on an automatic tracking total station (ATTS) measuring 53 retroreflective prisms across the study area every hour since May 2016. In general, the results are in agreement concerning the mean annual magnitude (ATTS: 6.4&amp;thinsp;cm within 2.2 years, 2.9&amp;thinsp;cm a<sup>&amp;minus;1</sup>; laser scanning data: 13.2&amp;thinsp;cm within 5.4 years, 2.4&amp;thinsp;cm a<sup>&amp;minus;1</sup>) and direction of the derived displacements. The analysis of the laser scanning data proved suitable for deriving long-term landslide displacements and can provide additional information about the deformation of single roofs.</p>

scholarly journals SOFTWARE COMPARISON FOR UNDERWATER ARCHAEOLOGICAL PHOTOGRAMMETRIC APPLICATIONS

2019 ◽  
Vol XLII-2/W15 ◽  
pp. 1195-1201
Author(s):  
M. Vlachos ◽  
L. Berger ◽  
R. Mathelier ◽  
P. Agrafiotis ◽  
D. Skarlatos
Keyword(s):  
3D Reconstruction ◽  
Point Cloud ◽  
Point Clouds ◽  
Bundle Adjustment ◽  
Archaeological Sites ◽  
Research Issues ◽  
Software Comparison ◽  
Object Distance ◽  
3D Point Clouds ◽  
Selection Of

<p><strong>Abstract.</strong> This paper presents an investigation as to whether and how the selection of the SfM-MVS software affects the 3D reconstruction of submerged archaeological sites. Specifically, Agisoft Photoscan, VisualSFM, SURE, 3D Zephyr and Reality Capture software were used and evaluated according to their performance in 3D reconstruction using specific metrics over the reconstructed underwater scenes. It must be clarified that the scope of this study is not to evaluate specific algorithms or steps that the various software use, but to evaluate the final results and specifically the generated 3D point clouds. To address the above research issues, a dataset from the ancient shipwreck, laying at 45 meters below sea level, is used. The dataset is composed of 19 images having very small camera to object distance (1 meter), and 42 images with higher camera to object distance (3 meters) images. Using a common bundle adjustment for all 61 images, a reference point cloud resulted from the lower dataset is used to compare it with the point clouds of the higher dataset generated using the different photogrammetric packages. Following that, a comparison regarding the number of total points, cloud to cloud distances, surface roughness, surface density and a combined 3D metric was done to evaluate and see which one performed the best.</p>

scholarly journals Estimating forest uniformity in Eucalyptus spp. and Pinus taeda L. stands using field measurements and structure from motion point clouds generated from unmanned aerial vehicle (UAV) data collection

2018 ◽  
Vol 27 (2) ◽  
pp. e005 ◽  
Author(s):  
Ângela M. K. Hentz ◽  
Carlos A. Silva ◽  
Ana P. Dalla Corte ◽  
Sylvio P. Netto ◽  
Michael P. Strager ◽  
...  
Keyword(s):  
Pinus Taeda ◽  
Unmanned Aerial Vehicle ◽  
Digital Terrain Model ◽  
Tree Height ◽  
Point Clouds ◽  
Forest Monitoring ◽  
Surface Model ◽  
3D Point Clouds ◽  
Aerial Vehicle ◽  
Pinus Taeda L

Aim of study: In this study we applied 3D point clouds generated by images obtained from an Unmanned Aerial Vehicle (UAV) to evaluate the uniformity of young forest stands.Area of study: Two commercial forest stands were selected, with two plots each. The forest species studied were Eucalyptus spp. and Pinus taeda L. and the trees had an age of 1.5 years.Material and methods: The individual trees were detected based on watershed segmentation and local maxima, using the spectral values stored in the point cloud. After the tree detection, the heights were calculated using two approaches, in the first one using the Digital Surface Model (DSM) and a Digital Terrain Model, and in the second using only the DSM. We used the UAV-derived heights to estimate an uniformity index.Main results: The trees were detected with a maximum 6% of error. However, the height was underestimated in all cases, in an average of 1 and 0.7 m for Pinus and Eucalyptus stands. We proposed to use the models built herein to estimate tree height, but the regression models did not explain the variably within the data satisfactorily. Therefore, the uniformity index calculated using the direct UAV-height values presented results close to the field inventory, reaching better results when using the second height approach (error ranging 2.8-7.8%).Research highlights: The uniformity index using the UAV-derived height from the proposed methods was close to the values obtained in field. We noted the potential for using UAV imagery in forest monitoring.

scholarly journals COMPARISON OF 3D POINT CLOUDS FROM AERIAL STEREO IMAGES AND LIDAR FOR FOREST CHANGE DETECTION

2017 ◽  
Vol XLII-3/W3 ◽  
pp. 1-5 ◽  
Author(s):  
D. Ali-Sisto ◽  
P. Packalen
Keyword(s):  
Classification Accuracy ◽  
Digital Terrain Model ◽  
Ground Level ◽  
Point Clouds ◽  
Aerial Image ◽  
Lidar Data ◽  
Forest Change ◽  
Time Points ◽  
3D Point Clouds ◽  
The Difference

This study compares performance of aerial image based point clouds (IPCs) and light detection and ranging (LiDAR) based point clouds in detection of thinnings and clear cuts in forests. IPCs are an appealing method to update forest resource data, because of their accuracy in forest height estimation and cost-efficiency of aerial image acquisition. We predicted forest changes over a period of three years by creating difference layers that displayed the difference in height or volume between the initial and subsequent time points. Both IPCs and LiDAR data were used in this process. The IPCs were constructed with the Semi-Global Matching (SGM) algorithm. Difference layers were constructed by calculating differences in fitted height or volume models or in canopy height models (CHMs) from both time points. The LiDAR-derived digital terrain model (DTM) was used to scale heights to above ground level. The study area was classified in logistic regression into the categories ClearCut, Thinning or NoChange with the values from the difference layers. We compared the predicted changes with the true changes verified in the field, and obtained at best a classification accuracy for clear cuts 93.1&amp;thinsp;% with IPCs and 91.7&amp;thinsp;% with LiDAR data. However, a classification accuracy for thinnings was only 8.0&amp;thinsp;% with IPCs. With LiDAR data 41.4&amp;thinsp;% of thinnings were detected. In conclusion, the LiDAR data proved to be more accurate method to predict the minor changes in forests than IPCs, but both methods are useful in detection of major changes.

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