Detection and 3D Reconstruction of Buildings from Aerial Images

2019 ◽  
Vol 45 (6) ◽  
pp. 311-318
Author(s):  
L. V. Novotortsev ◽  
A. G. Voloboy
Keyword(s):  
3D Reconstruction ◽  
Aerial Images

scholarly journals Deep Learning Segmentation and 3D Reconstruction of Road Markings Using Multiview Aerial Imagery

2019 ◽  
Vol 8 (1) ◽  
pp. 47 ◽  
Author(s):  
Franz Kurz ◽  
Seyed Azimi ◽  
Chun-Yu Sheu ◽  
Pablo d’Angelo
Keyword(s):  
3D Reconstruction ◽  
Autonomous Vehicles ◽  
Automatic Segmentation ◽  
Image Data ◽  
Autonomous Driving ◽  
Aerial Imagery ◽  
Aerial Images ◽  
Aerial Image ◽  
Matching Problem ◽  
Convolutional Network

The 3D information of road infrastructures is growing in importance with the development of autonomous driving. In this context, the exact 2D position of road markings as well as height information play an important role in, e.g., lane-accurate self-localization of autonomous vehicles. In this paper, the overall task is divided into an automatic segmentation followed by a refined 3D reconstruction. For the segmentation task, we applied a wavelet-enhanced fully convolutional network on multiview high-resolution aerial imagery. Based on the resulting 2D segments in the original images, we propose a successive workflow for the 3D reconstruction of road markings based on a least-squares line-fitting in multiview imagery. The 3D reconstruction exploits the line character of road markings with the aim to optimize the best 3D line location by minimizing the distance from its back projection to the detected 2D line in all the covering images. Results showed an improved IoU of the automatic road marking segmentation by exploiting the multiview character of the aerial images and a more accurate 3D reconstruction of the road surface compared to the semiglobal matching (SGM) algorithm. Further, the approach avoids the matching problem in non-textured image parts and is not limited to lines of finite length. In this paper, the approach is presented and validated on several aerial image data sets covering different scenarios like motorways and urban regions.

Model-Based 3D Reconstruction of Buildings from Multiple Aerial Images

Annals of GIS ◽  
2002 ◽  
Vol 8 (1) ◽  
pp. 16-23
Author(s):  
Yi-Hsing Tseng ◽  
Sendo Wang
Keyword(s):  
3D Reconstruction ◽  
Aerial Images ◽  
Model Based

An Acquisition and Distribution System for Situational Awareness

2003 ◽  
Author(s):  
William E. Green ◽  
Paul Y. Oh ◽  
Seunghyun Yoon
Keyword(s):  
Edge Detection ◽  
3D Reconstruction ◽  
Distribution System ◽  
Situational Awareness ◽  
Aerial Images ◽  
Mechanism Synthesis ◽  
Aerial Robots ◽  
First Response ◽  
Time Required ◽  
Aerial Platform

In times of disaster acquiring aerial images is challenging. Runways may be crippled thus denying conventional aircraft in the area from taking off. Also the time required to schedule a satellite fly-by may delay first response efforts. Man backpackable aerial robots can be carried close to the disaster site and flown to capture aerial images. This paper integrates mechatronics, intelligent sensing, and mechanism synthesis in a teleoperable kite-mounted camera. Rapidly deployable, transportable by foot, easy to fly and affordable, our system can quickly acquire, process and distribute aerial images. Images mosaicing edge detection, 3D reconstruction and geo-referencing resulting from images acquired by our aerial platform are also presented.

scholarly journals Hierarchical Clustering-Aligning Framework Based Fast Large-Scale 3D Reconstruction Using Aerial Imagery

2019 ◽  
Vol 11 (3) ◽  
pp. 315
Author(s):  
Xiuchuan Xie ◽  
Tao Yang ◽  
DongDong Li ◽  
Zhi Li ◽  
Yanning Zhang
Keyword(s):  
3D Reconstruction ◽  
Large Scale ◽  
Similarity Transformation ◽  
Estimation Method ◽  
Unmanned Aircraft ◽  
Aerial Images ◽  
Aerial Image ◽  
Image Clustering ◽  
Partial Structures ◽  
Loop Constraint

With extensive applications of Unmanned Aircraft Vehicle (UAV) in the field of remotesensing, 3D reconstruction using aerial images has been a vibrant area of research. However,fast large-scale 3D reconstruction is a challenging task. For aerial image datasets, large scale meansthat the number and resolution of images are enormous, which brings significant computationalcost to the 3D reconstruction, especially in the process of Structure from Motion (SfM). In thispaper, for fast large-scale SfM, we propose a clustering-aligning framework that hierarchicallymerges partial structures to reconstruct the full scene. Through image clustering, an overlappingrelationship between image subsets is established. With the overlapping relationship, we proposea similarity transformation estimation method based on joint camera poses of common images.Finally, we introduce the closed-loop constraint and propose a similarity transformation-based hybridoptimization method to make the merged complete scene seamless. The advantage of the proposedmethod is a significant efficiency improvement without a marginal loss in accuracy. Experimentalresults on the Qinling dataset captured over Qinling mountain covering 57 square kilometersdemonstrate the efficiency and robustness of the proposed method.

3D reconstruction of generic house roofs from aerial images of urban areas

1997 ◽  
Author(s):  
Jan Vandekerckhove ◽  
David Frere ◽  
Theo Moons ◽  
Luc J. Van Gool
Keyword(s):  
3D Reconstruction ◽  
Urban Areas ◽  
Aerial Images

scholarly journals 3D Reconstruction of Tree-Crown Based on the UAV Aerial Images

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Chao Xu ◽  
Zeping Lu ◽  
Guangquan Xu ◽  
Zhiyong Feng ◽  
Hongyan Tan ◽  
...  
Keyword(s):  
3D Reconstruction ◽  
3D Model ◽  
Local Area ◽  
Aerial Images ◽  
Mountainous Area ◽  
Feature Points ◽  
Watershed Segmentation ◽  
Tree Crown ◽  
L System ◽  
Contour Information

The algorithm for 3D reconstruction of tree-crown is presented with the UAV aerial images from a mountainous area in China. Considering the fact that the aerial images consist of little tree-crown texture and contour information, a feature area extraction method is proposed based on watershed segmentation, and the local area correlation coefficient is calculated to match the feature areas, in order to fully extract the characteristics that can reflect the structure of tree-crown. Then, the depth of feature points is calculated using the stereo vision theory. Finally, the L-system theory is applied to construct the 3D model of tree. The experiments are conducted with the tree-crown images from UAV aerial images manually. The experiment result showed that the method proposed in this paper can fully extract and match the feature points of tree-crown that can reconstruct the 3D model of the tree-crown correctly.

scholarly journals REAL-TIME 3D RECONSTRUCTION FROM IMAGES TAKEN FROM AN UAV

2015 ◽  
Vol XL-3/W3 ◽  
pp. 313-319 ◽  
Author(s):  
A. Zingoni ◽  
M. Diani ◽  
G. Corsini ◽  
A. Masini
Keyword(s):  
3D Reconstruction ◽  
Real Time ◽  
3D Model ◽  
Reconstruction Algorithm ◽  
3D Models ◽  
Aerial Images ◽  
Small Uav ◽  
Area Surveillance ◽  
Camera Parameters ◽  
Areas Of Interest

We designed a method for creating 3D models of objects and areas from two aerial images acquired from an UAV. The models are generated automatically and in real-time, and consist in dense and true-colour reconstructions of the considered areas, which give the impression to the operator to be physically present within the scene. The proposed method only needs a cheap compact camera, mounted on a small UAV. No additional instrumentation is necessary, so that the costs are very limited. The method consists of two main parts: the design of the acquisition system and the 3D reconstruction algorithm. In the first part, the choices for the acquisition geometry and for the camera parameters are optimized, in order to yield the best performance. In the second part, a reconstruction algorithm extracts the 3D model from the two acquired images, maximizing the accuracy under the real-time constraint. A test was performed in monitoring a construction yard, obtaining very promising results. Highly realistic and easy-to-interpret 3D models of objects and areas of interest were produced in less than one second, with an accuracy of about 0.5m. For its characteristics, the designed method is suitable for video-surveillance, remote sensing and monitoring, especially in those applications that require intuitive and reliable information quickly, as disasters monitoring, search and rescue and area surveillance.

scholarly journals DAMAGE ESTIMATION OF EXPLOSIONS IN URBAN ENVIRONMENTS BY SIMULATION

2019 ◽  
Vol XLII-3/W8 ◽  
pp. 253-260
Author(s):  
L. Mohr ◽  
R. Benauer ◽  
P. Leitl ◽  
F. Fraundorfer
Keyword(s):  
3D Reconstruction ◽  
Disaster Management ◽  
Structural Integrity ◽  
Cfd Simulation ◽  
Semantic Segmentation ◽  
Urban Environments ◽  
Aerial Images ◽  
Area Of Interest ◽  
Novel Approach ◽  
The Impact

<p><strong>Abstract.</strong> Precise models of the impact of explosions in urban environments provide novel and valuable information in disaster management for developing precautionary, preventive and mitigating measures. Yet to date, no methods enabling accurate predictions of the process and effect of detonations at particular locations exist. We propose a novel approach mitigating this gap by combining state-of-the-art methods from photogrammetric 3D reconstruction, semantic segmentation and computational based numerical simulations. In a first step, we create an accurate urban 3D reconstruction from georeferenced aerial images. The resulting city model is then enriched with semantic information obtained from the original source images as well as from registered terrestrial images using deep neural networks. This allows for an efficient automatic preparation of a 3D model suitable for the use as a geometry for the numerical investigations. Using this approach, we are able to provide recent and precise models of an area of interest in an automated fashion. Within the model, we are now able to define the explosive charge size and location and simulate the resulting blast wave propagation using CFD simulation. This provides a full estimation for the expected pressure propagation of a defined charge size. From these results, arising damages and their extent, as well as possible access routes or countermeasures, can be estimated. Using georeferenced sources allows for the integration and utilization of simulation results into existing geoinformation systems of disaster management units, providing novel inputs for training, preparation and prevention. We demonstrate our proposed approach by evaluating expected glass breakage and expected damages impairing the structural integrity of buildings depending on the charge size using a 3D reconstruction from aerial images of an area in the inner city of Graz, Austria.</p>

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