High Density Aerial Image Matching: State-of-the-Art and Future Prospects

Haala, N.; Cavegn, S.

doi:10.5194/isprsarchives-xli-b4-625-2016

Cited by 7 publications

(4 citation statements)

References 4 publications

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“…3D data generation often requires expensive data collection such as aerial photogrammetric or LiDAR flight [1,2]. Depending on the required accuracy, resolution and other specs of the final products, the efforts in data collection and processing can exponentially grow.…”

Section: Introductionmentioning

confidence: 99%

“…We introduce in our proposed method major contributions to address the abovementioned challenges to form a complete fusion pipeline. These contributions are: (1) we introduce a monocular video-frame-based 3D reconstruction pipeline to achieve the minimal geometric distortion by leveraging the speed and accuracy in a photogrammetric reconstruction pipeline called MetricSFM. (2) We introduce a cross-view geo-registration and fusion algorithm that takes point clouds generated from satellite multi-view stereo (MVS) images and from ground-view videos, to coregister the ground-view point clouds to the overview point clouds; (3) we extend a view-based meshing approach to accommodate point clouds with images coming from different cameras.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

3D Reconstruction through Fusion of Cross-View Images

Qin

Song²,

Ling³

et al. 2020

Recent Advances in Image Restoration With Applications to Real World Problems

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3D recovery from multi-stereo and stereo images, as an important application of the image-based perspective geometry, serves many applications in computer vision, remote sensing, and Geomatics. In this chapter, the authors utilize the imaging geometry and present approaches that perform 3D reconstruction from cross-view images that are drastically different in their viewpoints. We introduce our project work that takes ground-view images and satellite images for full 3D recovery, which includes necessary methods in satellite and ground-based point cloud generation from images, 3D data co-registration, fusion, and mesh generation. We demonstrate our proposed framework on a dataset consisting of twelve satellite images and 150 k video frames acquired through a vehicle-mounted Go-pro camera and demonstrate the reconstruction results. We have also compared our results with results generated from an intuitive processing pipeline that involves typical geo-registration and meshing methods.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

3D Reconstruction through Fusion of Cross-View Images

Qin

Song²,

Ling³

et al. 2020

Recent Advances in Image Restoration With Applications to Real World Problems

View full text Add to dashboard Cite

show abstract

“…City-scale data generation often requires expensive data collection such as aerial photogrammetric or LiDAR flight (Haala and Cavegn, 2016;Schwarz, 2010). Depending on the required accuracy, resolution, the efforts in data collection and processing can exponentially grow.…”

Section: Introductionmentioning

confidence: 99%

3d Data Generation Using Low-Cost Cross-View Images

Qin

Song

Huang

2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. 3D data generation often requires expensive data collection such as aerial photogrammetric or LiDAR flight. In cases such data are unavailable, for example, areas of interest inaccessible from aerial platforms, alternative sources to be considered can be quite heterogeneous and come in the form of different accuracy, resolution and views, which challenge the standard data processing workflows. Assuming only overview satellite and ground-level go-pro images are available, which we call cross-view data due to the significant view differences, this paper introduces a framework from our project, consisting of a few novel algorithms that convert such challenging dataset to 3D textured mesh models containing both top and façade features. The necessary methods include 3D point cloud generation from satellite overview images and ground-level images, geo-registration and meshing. We firstly introduce the problems and discuss the potential challenges and introduce our proposed methods to address these challenges. Finally, we practice our proposed framework on a dataset consisting of twelve satellite images and 150k video frames acquired through a vehicle-mounted Go-pro camera and demonstrate the reconstruction results. We have also compared our results with results generated from an intuitive processing pipeline that involves typical geo-registration and meshing methods.

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“…In these applications, point clouds filtering is still an essential step. Although the DIM point clouds may reach the high accuracy and resolution corresponding to the GSD (ground sampling distance, GSD) of the original images for rather complex urban environments (Haala and Cavegn, 2016;Remondino et al, 2014), there are still many differences between the DIM and Lidar point clouds. Some works (Rau et al, 2015;Themistocleous et al, 2016) have pointed that the differences contain the point density, variation.…”

Section: Introductionmentioning

confidence: 99%

An Automatic Filter Algorithm for Dense Image Matching Point Clouds

Dong

Zhang

Cui

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Although many filter algorithms have been presented over past decades, these algorithms are usually designed for the Lidar point clouds and can’t separate the ground points from the DIM (dense image matching, DIM) point clouds derived from the oblique aerial images owing to the high density and variation of the DIM point clouds completely. To solve this problem, a new automatic filter algorithm is developed on the basis of adaptive TIN models. At first, the differences between Lidar and DIM point clouds which influence the filtering results are analysed in this paper. To avoid the influences of the plants which can’t be penetrated by the DIM point clouds in the searching seed pointes process, the algorithm makes use of the facades of buildings to get ground points located on the roads as seed points and construct the initial TIN. Then a new densification strategy is applied to deal with the problem that the densification thresholds do not change as described in other methods in each iterative process. Finally, we use the DIM point clouds located in Potsdam produced by Photo-Scan to evaluate the method proposed in this paper. The experiment results show that the method proposed in this paper can not only separate the ground points from the DIM point clouds completely but also obtain the better filter results compared with TerraSolid. 1.

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High Density Aerial Image Matching: State-of-the-Art and Future Prospects

Cited by 7 publications

References 4 publications

3D Reconstruction through Fusion of Cross-View Images

3D Reconstruction through Fusion of Cross-View Images

3d Data Generation Using Low-Cost Cross-View Images

An Automatic Filter Algorithm for Dense Image Matching Point Clouds

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