Accurate and efficient ground-to-aerial model alignment

Gao, Xiang; Hu, Lihua; Cui, Hainan; Shen, Shuhan; Hu, Zhanyi

doi:10.1016/j.patcog.2017.11.003

Cited by 20 publications

(13 citation statements)

References 19 publications

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“…• Multi-source 3D data fusion. Few attempts have been carried out in the fusion of aerial and ground-based 3D point clouds or models [124]. The large differences in camera viewpoints and scales impose a tricky issue to the alignment of aerial and ground 3D data.…”

Section: Dense Reconstructionmentioning

confidence: 99%

Mini-Unmanned Aerial Vehicle-Based Remote Sensing: Techniques, applications, and prospects

Xiang

Xia

Zhang

2019

IEEE Geosci. Remote Sens. Mag.

140

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The past few decades have witnessed the great progress of unmanned aircraft vehicles (UAVs) in civilian fields, especially in photogrammetry and remote sensing. In contrast with the platforms of manned aircraft and satellite, the UAV platform holds many promising characteristics: flexibility, efficiency, highspatial/temporal resolution, low cost, easy operation, etc., which make it an effective complement to other remote-sensing platforms and a cost-effective means for remote sensing. Considering the popularity and expansion of UAV-based remote sensing in recent years, this paper provides a systematic survey on the recent advances and future prospectives of UAVs in the remote-sensing community. Specifically, the main challenges and key technologies of remote-sensing data processing based on UAVs are discussed and summarized firstly. Then, we provide an overview of the widespread applications of UAVs in remote sensing. Finally, some prospects for future work are discussed. We hope this paper will provide remotesensing researchers an overall picture of recent UAV-based remote sensing developments and help guide the further research on this topic.

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Section: Dense Reconstructionmentioning

confidence: 99%

Mini-Unmanned Aerial Vehicle-Based Remote Sensing: Techniques, applications, and prospects

Xiang

Xia

Zhang

2019

IEEE Geosci. Remote Sens. Mag.

140

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“…To further evaluate the generalization ability of the proposed LMPG in different scenes, we also included those public datasets in experiments. As mentioned in [10], few largescale publicly available datasets have ground and aerial images for the same architectural scene. Many released datasets are specifically meant for the task of urban 3D reconstruction, and each dataset only contains complete ground and aerial image pairs for one building.…”

Section: A Datasetsmentioning

confidence: 99%

“…Image matching aims to find correspondence between two or multiple overlapped images, which have been extensively studied under different baselines and scenarios [9], [10], [11], [12], [13], [14]. The problem of image matching is typically solved by three steps: local feature extraction and description, putative match construction, and mismatch removal.…”

Section: Introductionmentioning

confidence: 99%

“…It is therefore particularly hard for existing mismatch removal methods to create a separability constraint for such a putative match set. A different way of matching local features between ground and aerial images is to warp one view into another by using camera pose and 3D geometric information [3], [4], [10], and performing feature matching on synthetic images. Some of these methods have been successfully applied in practical applications.…”

Section: Introductionmentioning

confidence: 99%

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Lattice-Point Mutually Guided Ground-to-Aerial Feature Matching for Urban Scene Images

Zheng

Xiong

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Ground-to-aerial feature matching bridges information from cross-view images, which enables optimized urban applications, e.g., pixel-level geolocating and complete urban 3D reconstruction. However, urban ground and aerial images typically suffer from drastic changes in viewpoint, scale, and illumination, together with repetitive patterns. Thus, direct matching of local features between ground and aerial images is particularly difficult because of the low similarity of local descriptors and high ambiguity in true-false match discrimination. For this challenging task, we propose a novel lattice-point mutually guided matching (LPMG) method in this paper. We specifically address two key issues: 1) reducing descriptor variance and 2) enhancing true-false match discriminability. The former is solved by recovering the geometry and appearance of the underlying image region in 3D through automatic view rectification on ground and aerial images. The latter is circumvented by replacing the conventional mismatch removal with an LPMG strategy. In this strategy, the topology structure of repeated façade elements (i.e., lattice), and the high reliable point matching seeds, are first extracted from the rectified ground and aerial images. Then, the point matching seeds guide the self-similar lattice tiles from two views to be precisely aligned, thereby estimating an accurate transformation model from lattice tile correspondences. Finally, the estimated model powerfully supervises the differentiation of true and false matches from the entire putative match set. Extensive experiments conducted on several datasets show that our method can obtain a considerable number of nearly pure correct matches from urban ground and aerial images, significantly outperforming those existing methods.

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“…Some of the researchers have tried to correct the differences in view angle to allow for matching while using classical local features. One category of methods is based on the camera pose and 3D geometric information (e.g., stereo models [21], depth maps [22], and 3D sparse meshes [23]) to generate synthetic images that are approximately aligned to the viewpoint of the target image, and thus realize view-dependent SIFT matching. However, these methods require the reconstruction of the dense 3D geometry for the images, and they are strongly restricted by the quality of the reconstructed 3D information.…”

Section: Introductionmentioning

confidence: 99%

Distorted Building Image Matching with Automatic Viewpoint Rectification and Fusion

Yue

Zheng

2019

Sensors

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Building image-matching plays a critical role in the urban applications. However, finding reliable and sufficient feature correspondences between the real-world urban building images that were captured in widely separate views are still challenging. In this paper, we propose a distorted image matching method combining the idea of viewpoint rectification and fusion. Firstly, the distorted images are rectified to the standard view with the transform invariant low-rank textures (TILT) algorithm. A local symmetry feature graph is extracted from the building images, followed by multi-level clustering using the mean shift algorithm, to automatically detect the low-rank texture region. After the viewpoint rectification, the Oriented FAST and Rotated BRIEF (ORB) feature is used to match the images. The grid-based motion statistics (GMS) and RANSAC techniques are introduced to remove the outliers and preserve the correct matching points to deal with the mismatched pairs. Finally, the matching results for the rectified views are projected to the original viewpoint space, and the matches before and after distortion rectification are fused to further determine the final matches. The experimental results show that both the number of matching pairs and the matching precision for the distorted building images can be significantly improved while using the proposed method.

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Accurate and efficient ground-to-aerial model alignment

Cited by 20 publications

References 19 publications

Mini-Unmanned Aerial Vehicle-Based Remote Sensing: Techniques, applications, and prospects

Mini-Unmanned Aerial Vehicle-Based Remote Sensing: Techniques, applications, and prospects

Lattice-Point Mutually Guided Ground-to-Aerial Feature Matching for Urban Scene Images

Distorted Building Image Matching with Automatic Viewpoint Rectification and Fusion

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