scholarly journals FUSION AND EVALUATION OF 3D DATA AND EXCAVATION DOCUMENTS FOR COMPARISON OF ORIGINAL AND DIGITAL COPY IN THE CASE OF A MEGALITHIC TOMB

2021 ◽  
Vol XLVI-M-1-2021 ◽  
pp. 647-651
Author(s):  
F. Russell ◽  
P. Kalinowski ◽  
F. Both ◽  
T. Luhmann ◽  
U. Warnke
Keyword(s):  
Point Cloud ◽  
3D Model ◽  
Historical Data ◽  
Image Data ◽  
Current Situation ◽  
Communication To The Public ◽  
The Public ◽  
3D Data ◽  
Multidisciplinary Cooperation ◽  
Historical Image

Abstract. This contribution shows the preliminary results of the multidisciplinary cooperation of archaeological, technical and heritage issues using the example of the megalithic tomb Kleinenkneten II. The tomb was excavated in the 1930s, but big parts of the documentation have unfortunately been destroyed. Furthermore, some ancient interpretations need to be objectively reviewed. More than 500 historical image data visually document the historical excavation situation. In addition, the current situation was recorded in 3D using modern methods. Geodetic products, such as orthophotos, can be derived from modern data and compared with old plans. Also, a point cloud was calculated from historical images, which can be compared with the current situation. The combination of modern and historical data enables new archaeological interpretations. From a museum perspective, strategies for the construction of authentic value of the 3D model is considered, as well as its communication to the public.

scholarly journals AUTOMATIC CO-REGISTRATION OF AERIAL IMAGERY AND UNTEXTURED MODEL DATA UTILIZING AVERAGE SHADING GRADIENTS

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 581-588 ◽  
Author(s):  
S. Schmitz ◽  
M. Weinmann ◽  
B. Ruf
Keyword(s):  
3D Model ◽  
Image Data ◽  
Aerial Imagery ◽  
Aerial Photographs ◽  
Model Data ◽  
Mesh Model ◽  
3D Data ◽  
Reprojection Error ◽  
Camera Pose ◽  
2D And 3D

<p><strong>Abstract.</strong> The comparison of current image data with existing 3D model data of a scene provides an efficient method to keep models up to date. In order to transfer information between 2D and 3D data, a preliminary co-registration is necessary. In this paper, we present a concept to automatically co-register aerial imagery and untextured 3D model data. To refine a given initial camera pose, our algorithm computes dense correspondence fields using SIFT flow between gradient representations of the model and camera image, from which 2D&amp;ndash;3D correspondences are obtained. These correspondences are then used in an iterative optimization scheme to refine the initial camera pose by minimizing the reprojection error. Since it is assumed that the model does not contain texture information, our algorithm is built up on an existing method based on Average Shading Gradients (ASG) to generate gradient images based on raw geometry information only. We apply our algorithm for the co-registering of aerial photographs to an untextured, noisy mesh model. We have investigated different magnitudes of input error and show that the proposed approach can reduce the final reprojection error to a minimum of 1.27&amp;thinsp;&amp;plusmn;&amp;thinsp;0.54 pixels, which is less than 10% of its initial value. Furthermore, our evaluation shows that our approach outperforms the accuracy of a standard Iterative Closest Point (ICP) implementation.</p>

scholarly journals 3D DATA ACQUISITION BASED ON OPENCV FOR CLOSE-RANGE PHOTOGRAMMETRY APPLICATIONS

2017 ◽  
Vol XLII-1/W1 ◽  
pp. 377-382 ◽  
Author(s):  
L. Jurjević ◽  
M. Gašparović
Keyword(s):  
Traffic Management ◽  
Point Cloud ◽  
3D Model ◽  
Point Clouds ◽  
Model Reconstruction ◽  
Close Range Photogrammetry ◽  
3D Data ◽  
3D Model Reconstruction ◽  
Close Range ◽  
Real Time Application

Development of the technology in the area of the cameras, computers and algorithms for 3D the reconstruction of the objects from the images resulted in the increased popularity of the photogrammetry. Algorithms for the 3D model reconstruction are so advanced that almost anyone can make a 3D model of photographed object. The main goal of this paper is to examine the possibility of obtaining 3D data for the purposes of the close-range photogrammetry applications, based on the open source technologies. All steps of obtaining 3D point cloud are covered in this paper. Special attention is given to the camera calibration, for which two-step process of calibration is used. Both, presented algorithm and accuracy of the point cloud are tested by calculating the spatial difference between referent and produced point clouds. During algorithm testing, robustness and swiftness of obtaining 3D data is noted, and certainly usage of this and similar algorithms has a lot of potential in the real-time application. That is the reason why this research can find its application in the architecture, spatial planning, protection of cultural heritage, forensic, mechanical engineering, traffic management, medicine and other sciences.

scholarly journals DUBAI 3D TEXTUERD MESH USING HIGH QUALITY RESOLUTION VERTICAL/OBLIQUE AERIAL IMAGERY

2016 ◽  
Vol XLI-B3 ◽  
pp. 151-154
Author(s):  
Adib Tayeb Madani ◽  
Abdullateef Ziad Ahmad ◽  
Lueken Christoph ◽  
Zamzam Hammadi ◽  
Manal Abdullah x Manal Abdullah Sabeal
Keyword(s):  
Point Cloud ◽  
3D Model ◽  
City Planning ◽  
Spatial Information ◽  
Visual Assessment ◽  
Urban Environments ◽  
3D Analysis ◽  
High Quality ◽  
3D Data ◽  
Global Matching

Providing high quality 3D data with reasonable quality and cost were always essential, affording the core data and foundation for developing an information-based decision-making tool of urban environments with the capability of providing decision makers, stakeholders, professionals, and public users with 3D views and 3D analysis tools of spatial information that enables real-world views. Helps and assist in improving users’ orientation and also increase their efficiency in performing their tasks related to city planning, Inspection, infrastructures, roads, and cadastre management. In this paper, the capability of multi-view Vexcel UltraCam Osprey camera images is examined to provide a 3D model of building façades using an efficient image-based modeling workflow adopted by commercial software’s. The main steps of this work include: Specification, point cloud generation, and 3D modeling. After improving the initial values of interior and exterior parameters at first step, an efficient image matching technique such as Semi Global Matching (SGM) is applied on the images to generate point cloud. Then, a mesh model of points is calculated using and refined to obtain an accurate model of buildings. Finally, a texture is assigned to mesh in order to create a realistic 3D model. The resulting model has provided enough LoD2 details of the building based on visual assessment. The objective of this paper is neither comparing nor promoting a specific technique over the other and does not mean to promote a sensor-based system over another systems or mechanism presented in existing or previous paper. The idea is to share experience.

scholarly journals HYBRID GEOREFERENCING, ENHANCEMENT AND CLASSIFICATION OF ULTRA-HIGH RESOLUTION UAV LIDAR AND IMAGE POINT CLOUDS FOR MONITORING APPLICATIONS

2020 ◽  
Vol V-2-2020 ◽  
pp. 727-734
Author(s):  
N. Haala ◽  
M. Kölle ◽  
M. Cramer ◽  
D. Laupheimer ◽  
G. Mandlburger ◽  
...  
Keyword(s):  
Point Cloud ◽  
Image Data ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Image Texture ◽  
Image Point ◽  
Joint Orientation ◽  
3D Point Clouds ◽  
3D Data

Abstract. This paper presents a study on the potential of ultra-high accurate UAV-based 3D data capture by combining both imagery and LiDAR data. Our work is motivated by a project aiming at the monitoring of subsidence in an area of mixed use. Thus, it covers built-up regions in a village with a ship lock as the main object of interest as well as regions of agricultural use. In order to monitor potential subsidence in the order of 10 mm/year, we aim at sub-centimeter accuracies of the respective 3D point clouds. We show that hybrid georeferencing helps to increase the accuracy of the adjusted LiDAR point cloud by integrating results from photogrammetric block adjustment to improve the time-dependent trajectory corrections. As our main contribution, we demonstrate that joint orientation of laser scans and images in a hybrid adjustment framework significantly improves the relative and absolute height accuracies. By these means, accuracies corresponding to the GSD of the integrated imagery can be achieved. Image data can also help to enhance the LiDAR point clouds. As an example, integrating results from Multi-View Stereo potentially increases the point density from airborne LiDAR. Furthermore, image texture can support 3D point cloud classification. This semantic segmentation discussed in the final part of the paper is a prerequisite for further enhancement and analysis of the captured point cloud.

Integration of Image and Laser Scanning Data Based on Selected Example

2014 ◽  
Vol 19 (2-3) ◽  
pp. 37-44 ◽  
Author(s):  
Sławomir Mikrut ◽  
Agnieszka Moskal ◽  
Urszula Marmol
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
3D Model ◽  
Image Data ◽  
Point Clouds ◽  
Model Reconstruction ◽  
Railway Line ◽  
3D Model Reconstruction ◽  
Conducting Research ◽  
The Village

Abstract The paper aims at presentation of results of research on integration of image and laser data based on selected example. Since a few years the authors have been conducting research on processing image data, and those obtained from laser scanning in the form of the so-called point cloud. In experiments data from terrestrial and mobile laser scanning gained for two different objects were compared: a parish house from Goźlice located in the open-air ethnographic museum at the village of Tokarnia, Poland, and part of the Cracow-Warsaw railway line. The results of those experiments proved that data in the form of point cloud were not always sufficient for a precise 3D model reconstruction. Supplementing point clouds with photogrammetric images seems to be the best solution.

scholarly journals EXPLORATORY STUDY OF 3D POINT CLOUD TRIANGULATION FOR SMART CITY MODELLING AND VISUALIZATION

2020 ◽  
Vol XLIV-4/W3-2020 ◽  
pp. 71-79
Author(s):  
S. A. M. Ariff ◽  
S. Azri ◽  
U. Ujang ◽  
A. A. M. Nasir ◽  
N. Ahmad Fuad ◽  
...  
Keyword(s):  
Point Cloud ◽  
Large Scale ◽  
3D Model ◽  
Large Data ◽  
3D Point Cloud ◽  
3D Mesh ◽  
Mesh Model ◽  
3D Data ◽  
3D Mesh Model ◽  
Hardware Specification

Abstract. The current trends of 3D scanning technologies allow us to acquire accurate 3D data of large-scale environment efficiently. The 3D data of large-scale environments is essential when generating 3D model is for the visualization of smart cities. For the seamless visualization of 3D model, large data size will be used during the 3D data acquisition. However, the processing time for large data size is time consuming and requires suitable hardware specification. In this study, different hardware capability in processing large data of 3D point cloud for mesh generation is investigated. Light Detection and Ranging (LiDAR) Airborne and Mobile Mapping System (MMS) are used as data input and processed using Bentley ContextCapture software. The study is conducted in Malaysia, specifically in Wilayah Persekutuan Kuala Lumpur and Selangor with the size of 49 km2. Several analyses have been performed to analyse the software and hardware specification based on the 3D mesh model generated. From the finding, we have suggested the most suitable hardware specification for 3D mesh model generation.

scholarly journals DUBAI 3D TEXTUERD MESH USING HIGH QUALITY RESOLUTION VERTICAL/OBLIQUE AERIAL IMAGERY

2016 ◽  
Vol XLI-B3 ◽  
pp. 151-154 ◽  
Author(s):  
Adib Tayeb Madani ◽  
Abdullateef Ziad Ahmad ◽  
Lueken Christoph ◽  
Zamzam Hammadi ◽  
Manal Abdullah x Manal Abdullah Sabeal
Keyword(s):  
Point Cloud ◽  
3D Model ◽  
City Planning ◽  
Spatial Information ◽  
Visual Assessment ◽  
Urban Environments ◽  
3D Analysis ◽  
High Quality ◽  
3D Data ◽  
Global Matching

Providing high quality 3D data with reasonable quality and cost were always essential, affording the core data and foundation for developing an information-based decision-making tool of urban environments with the capability of providing decision makers, stakeholders, professionals, and public users with 3D views and 3D analysis tools of spatial information that enables real-world views. Helps and assist in improving users’ orientation and also increase their efficiency in performing their tasks related to city planning, Inspection, infrastructures, roads, and cadastre management. In this paper, the capability of multi-view Vexcel UltraCam Osprey camera images is examined to provide a 3D model of building façades using an efficient image-based modeling workflow adopted by commercial software’s. The main steps of this work include: Specification, point cloud generation, and 3D modeling. After improving the initial values of interior and exterior parameters at first step, an efficient image matching technique such as Semi Global Matching (SGM) is applied on the images to generate point cloud. Then, a mesh model of points is calculated using and refined to obtain an accurate model of buildings. Finally, a texture is assigned to mesh in order to create a realistic 3D model. The resulting model has provided enough LoD2 details of the building based on visual assessment. The objective of this paper is neither comparing nor promoting a specific technique over the other and does not mean to promote a sensor-based system over another systems or mechanism presented in existing or previous paper. The idea is to share experience.

scholarly journals COMPARATIVE ANALYSIS OF VARIOUS CAMERA INPUT FOR VIDEOGRAMMETRY

2019 ◽  
Vol XLII-4/W16 ◽  
pp. 63-70
Author(s):  
N. Ahmad ◽  
S. Azri ◽  
U. Ujang ◽  
M. G. Cuétara ◽  
G. M. Retortillo ◽  
...  
Keyword(s):  
Comparative Analysis ◽  
Data Acquisition ◽  
Point Cloud ◽  
3D Model ◽  
Video Recording ◽  
Digital Camera ◽  
Point Clouds ◽  
Video Frame ◽  
3D Data

Abstract. Videogrammetry is a technique to generate point clouds by using video frame sequences. It is a branch of photogrammetry that offers an attractive capabilities and make it an interesting choice for a 3D data acquisition. However, different camera input and specification will produce different quality of point cloud. Thus, it is the aim of this study to investigate the quality of point cloud that is produced from various camera input and specification. Several devices are using in this study such as Iphone 5s, Iphone 7+, Iphone X, Digital camera of Casio Exilim EX-ZR1000 and Nikon D7000 DSLR. For each device, different camera with different resolution and frame per second (fps) are used for video recording. The videos are processed using EyesCloud3D by eCapture. EyesCloud3D is a platform that receive input such as videos and images to generate point clouds. 3D model is constructed based on generated point clouds. The total number of point clouds produced is analyzed to determine which camera input and specification produce a good 3D model. Besides that, factor of generating number of point clouds is analyzed. Finally, each camera resolution and fps is suggested for certain applications based on generated number of point cloud.

Registration and Fusion of UAV LiDAR System Sequence Images and Laser Point Clouds

2020 ◽  
Author(s):  
Jiayong Yu ◽  
Longchen Ma ◽  
Maoyi Tian, ◽  
Xiushan Lu
Keyword(s):  
Point Cloud ◽  
Image Data ◽  
Point Clouds ◽  
Optical Image ◽  
Point Cloud Data ◽  
Feature Points ◽  
Lidar System ◽  
Registration Accuracy ◽  
Cloud Data ◽  
Intensity Image

The unmanned aerial vehicle (UAV)-mounted mobile LiDAR system (ULS) is widely used for geomatics owing to its efficient data acquisition and convenient operation. However, due to limited carrying capacity of a UAV, sensors integrated in the ULS should be small and lightweight, which results in decrease in the density of the collected scanning points. This affects registration between image data and point cloud data. To address this issue, the authors propose a method for registering and fusing ULS sequence images and laser point clouds, wherein they convert the problem of registering point cloud data and image data into a problem of matching feature points between the two images. First, a point cloud is selected to produce an intensity image. Subsequently, the corresponding feature points of the intensity image and the optical image are matched, and exterior orientation parameters are solved using a collinear equation based on image position and orientation. Finally, the sequence images are fused with the laser point cloud, based on the Global Navigation Satellite System (GNSS) time index of the optical image, to generate a true color point cloud. The experimental results show the higher registration accuracy and fusion speed of the proposed method, thereby demonstrating its accuracy and effectiveness.

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