摘要 :
To produce a complete 3D reconstruction of a large-scale architectural scene, both ground and aerial images are usually captured. A common approach is to first reconstruct the models from different image sources separately, and al...
展开
To produce a complete 3D reconstruction of a large-scale architectural scene, both ground and aerial images are usually captured. A common approach is to first reconstruct the models from different image sources separately, and align thetn afterwards. Using this pipeline, this work proposes an accurate and efficient approach for ground-to-aerial model alignment in a coarse-to-fine manner. First, both the ground model and aerial model are transformed into the geo-referenced coordinate system using GPS meta-information for coarse alignment. Then, the coarsely aligned models are refined by a similarity transformation that is estimated based on 3D point correspondences between them, and the 3D point correspondences are determined in a 2D-image-matching manner by considering the rich textural and contextual information in the 2D images. Due to the dramatic differences in viewpoint and scale between ground and aerial images, which make matching them directly nearly impossible, we perform an intermediate view-synthesis step to mitigate the matching difficulty. To this end, the following three key issues are addressed: (a) selecting a suitable subset of aerial images to cover the ground model properly; (b) synthesizing images from the ground model under the viewpoints of the selected aerial images; and finally, (c) obtaining the 2D point matches between the synthesized images and the selected aerial images. The experimental results show that the proposed model alignment approach is quite effective and outperforms several state-of-the-art techniques in terms of both accuracy and efficiency. (C) 2017 Elsevier Ltd. All rights reserved.
收起
摘要 :
Ancient Chinese architecture 3D digitalization and documentation is a challenging task for the image based modeling community due to its architectural complexity and structural delicacy. Currently, an effective approach to ancient...
展开
Ancient Chinese architecture 3D digitalization and documentation is a challenging task for the image based modeling community due to its architectural complexity and structural delicacy. Currently, an effective approach to ancient Chinese architecture 3D reconstruction is to merge the two point clouds, separately obtained from ground and aerial images by the SfM technique. There are two understanding issues should be specially addressed: (1) it is difficult to find the point matches between the images from different sources due to their remarkable variations in viewpoint and scale; (2) due to the inevitable drift phenomenon in any SfM reconstruction process, the resulting two point clouds are no longer strictly related by a single similarity transformation as it should be theoretically. To address these two issues, a new point cloud merging method is proposed in this work. Our method has the following characteristics: (1) the images are matched by leveraging sparse mesh based image synthesis; (2) the putative point matches are filtered by geometrical consistency check and geometrical model verification; and (3) the two point clouds are merged via bundle adjustment by linking the ground-to-aerial tracks. Extensive experiments show that our method outperforms many of the state-of-the-art approaches in terms of ground-to-aerial image matching and point cloud merging.
收起
摘要 :
The capability of associating an image to its geographical location is a significant concern in journalism and digital forensics. Given the availability of geo-tagged satellite imagery for most of the Earth's surface, retrieving t...
展开
The capability of associating an image to its geographical location is a significant concern in journalism and digital forensics. Given the availability of geo-tagged satellite imagery for most of the Earth's surface, retrieving the location of a generic picture can be addressed as a cross-view image matching between aerial and ground views. In this paper, we outline some initial steps toward the development of a fully-unsupervised algorithm for ground-to-aerial image matching, exploiting the view-invariant adjacency relationships of the landmarks appearing in both views. We introduce a graph-based strategy that, given a set of pre-extracted landmarks, localizes the viewpoint of a ground-level 360-degree image within a broad aerial view of the same area, by matching the respective landmark graphs according to a specifically designed likelihood model.
收起
摘要 :
In this paper, we propose a novel method to precisely match two aerial images that were obtained in different environments via a two-stream deep network. By internally augmenting the target image, the network considers the two-str...
展开
In this paper, we propose a novel method to precisely match two aerial images that were obtained in different environments via a two-stream deep network. By internally augmenting the target image, the network considers the two-stream with the three input images and reflects the additional augmented pair in the training. As a result, the training process of the deep network is regularized and the network becomes robust for the variance of aerial images. Furthermore, we introduce an ensemble method that is based on the bidirectional network, which is motivated by the isomorphic nature of the geometric transformation. We obtain two global transformation parameters without any additional network or parameters, which alleviate asymmetric matching results and enable significant improvement in performance by fusing two outcomes. For the experiment, we adopt aerial images from Google Earth and the International Society for Photogrammetry and Remote Sensing (ISPRS). To quantitatively assess our result, we apply the probability of correct keypoints (PCK) metric, which measures the degree of matching. The qualitative and quantitative results show the sizable gap of performance compared to the conventional methods for matching the aerial images. All code and our trained model, as well as the dataset are available online.
收起
摘要 :
This paper presents a dataset recorded on-board a camera-equipped micro aerial vehicle flying within the urban streets of Zurich, Switzerland, at low altitudes (i.e. 5-15 m above the ground). The 2 km dataset consists of time sync...
展开
This paper presents a dataset recorded on-board a camera-equipped micro aerial vehicle flying within the urban streets of Zurich, Switzerland, at low altitudes (i.e. 5-15 m above the ground). The 2 km dataset consists of time synchronized aerial high-resolution images, global position system and inertial measurement unit sensor data, ground-level street view images, and ground truth data. The dataset is ideal to evaluate and benchmark appearance-based localization, monocular visual odometry, simultaneous localization and mapping, and online three-dimensional reconstruction algorithms for micro aerial vehicles in urban environments.
收起
摘要 :
ABSTRACT The fine 3D model is the essential spatial information for the construction of a smart city. UAV aerial images with large-scale scene perception ability are common data sources for 3D modelling of cities at present. Howev...
展开
ABSTRACT The fine 3D model is the essential spatial information for the construction of a smart city. UAV aerial images with large-scale scene perception ability are common data sources for 3D modelling of cities at present. However, in some complex urban areas, a single aerial image is difficult to capture the 3D scene information because of the existence of some problems such as inaccurate edges, holes, and blurred building facade textures due to changes in perspective and area occlusion. Therefore, how to solve perspective changes and area occlusion of the aerial image quickly and efficiently has become an important problem. The ground image can be used as an important supplement to solve the problem of missing bottom and area occlusion in oblique photography modelling. Thus, this article proposes a progressive matching method via multi-scale context feature coding network to achieve robust matching of aerial-ground remote sensing images, which provides better technical support for urban modelling. The main idea consists of three parts: (1) a multi-scale context feature coding network is designed to extract feature on aerial-ground images efficiently; (2) a block-based matching strategy is proposed to pay more attention to local features of the aerial-ground images; (3) a progressive matching method is applied in block matching stage to obtain more accurate features. We used eight sets of typical data, such as aerial images captured by the drone DJI-MAVIC2 and ground images captured by handheld devices as experimental objects, and compared them with algorithms such as SIFT, D2-net, DFM and SuperGlue. Experimental results show that our proposed aerial-ground image matching method has a good performance that the average NCM has improved 2.1–8.2 times, and the average rate of correct matching has an average increase of 26% points with the average root of mean square error is only 1.48 pixels.
收起
摘要 :
Increasing the availability of Unmanned Aerial Vehicles (UAV’s) platforms leads to a variety of applications for aerial exploration, surveillance, and transport. Many of these applications rely on the communication between the UA...
展开
Increasing the availability of Unmanned Aerial Vehicles (UAV’s) platforms leads to a variety of applications for aerial exploration, surveillance, and transport. Many of these applications rely on the communication between the UAV and the ground receiver which is subjected to high mobility that may lead to restrictions on link connectivity and throughput. In order to design high throughput and efficient communication schemes for these scenarios, a deep understanding of the communication channel behavior is required, especially taking into account measurement data from flight experiments. Channel propagation in urban environments involves diffraction effects which modify the Line-of-Sight (LoS) contribution of the total received signal, especially when the receiver is located on the ground. This process leads to scenarios where Multiple-Input Multiple-Output (MIMO) signal processing can take advantage from this situation. In this context, the goal of this paper is to study the diffraction effects of the LoS component through spatial correlation metrics of the signal. To accomplish this, we propose the use of a geometric stochastic technique to model the channel behavior which lies between High Altitude Platforms (HAP) and terrestrial link communications.
收起
摘要 :
Completeness and accuracy are two important factors in image-based indoor scene 3D reconstruction. Thus, an efficient image capturing scheme that could completely cover the scene, and a robust reconstruction method that could accu...
展开
Completeness and accuracy are two important factors in image-based indoor scene 3D reconstruction. Thus, an efficient image capturing scheme that could completely cover the scene, and a robust reconstruction method that could accurately reconstruct the scene are required. To this end, in this article we propose a new pipeline for indoor scene capturing and reconstruction using a mini drone and a ground robot, which takes both capturing completeness and reconstruction accuracy into consideration. First, we use a mini drone to capture aerial video of the indoor scene, from which a 3D aerial map is reconstructed. Then, the robot moving path is planned and a set of ground-view reference images are synthesized from the aerial map. After that, the robot enters the scene and captures ground video autonomously while using the reference images to locate its position during the movement. Finally, the ground and aerial images, which are adaptively extracted from the captured videos, are merged to reconstruct a complete and accurate indoor scene model. Experimental results on two indoor scenes demonstrate the effectiveness and robustness of our proposed indoor scene capturing and reconstruction pipeline.
收起
摘要 :
Interest in high-altitude platforms (HAPs) has been increasing recently, especially with the rapid technical development in solar panels' efficiency, energy storage, antenna design, and lightweight materials for aircraft parts. Th...
展开
Interest in high-altitude platforms (HAPs) has been increasing recently, especially with the rapid technical development in solar panels' efficiency, energy storage, antenna design, and lightweight materials for aircraft parts. These factors make high-altitude platforms more applicable in a wide variety of military, security, relief, and civilian applications. This paper provides overview on the high-altitude platforms and their advantages compared terrestrial and satellite communications. This paper also surveys the air-to-ground channel model used for HAPs, channel performance metrics, and optimizing various HAPs parameters..
收起
摘要 :
The target-location problems of observation and combat-integrated UAVs utilized in battles makes image matching challenging and of vital significance. This paper presents a framework of image matching based on region partitioning ...
展开
The target-location problems of observation and combat-integrated UAVs utilized in battles makes image matching challenging and of vital significance. This paper presents a framework of image matching based on region partitioning for target-image location, working on complex simulated aerial images consisting of, for example, scale-changing, rotation-changing, blurred, and occlusion images. Originally, an image-evaluation approach based on a weighted-orientation histogram was proposed to judge whether the image is an image with good texture or a textureless image. Two approaches based on layered architecture are employed for images with good texture and textureless images. In these two approaches, an improved SIFT image-matching algorithm incorporating detected Harris corners into the keypoint set is suggested, and Bhattacharyya distance based on an orientation histogram was employed to select the best result among different region pairs. Experiment results illustrated that the image-matching approach based on image segmentation has a much higher rate of 42.04 when compared to the traditional approach.
收起
原文获取