Quality Analysis on 3d Buidling Models Reconstructed From Uav Imagery

Jarząbek-Rychard, Małgorzata; Karpina, Mateusz

doi:10.5194/isprs-archives-xli-b1-1121-2016

Cited by 8 publications

(7 citation statements)

References 17 publications

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“…UAVs can be considered as a tool of short-and close-range data acquisition from small areas [9], which leads to images with small GSDs. This data has the potential for fast updates of the reconstructed 3D city models with low costs [10] and large-scale spatial data [11]. The combination of UAS imagery with computer vision and photogrammetric techniques generates high accuracy point clouds, even with end-user, small format cameras [8].…”

Section: Introductionmentioning

confidence: 99%

Window Detection from UAS-Derived Photogrammetric Point Cloud Employing Density-Based Filtering and Perceptual Organization

et al. 2018

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Point clouds with ever-increasing volume are regular data in 3D city modelling, in which building reconstruction is a significant part. The photogrammetric point cloud, generated from UAS (Unmanned Aerial System) imagery, is a novel type of data in building reconstruction. Its positive characteristics, alongside its challenging qualities, provoke discussions on this theme of research. In this paper, patch-wise detection of the points of window frames on facades and roofs are undertaken using this kind of data. A density-based multi-scale filter is devised in the feature space of normal vectors to globally handle the matter of high volume of data and to detect edges. Color information is employed for the downsized data to remove the inner clutter of the building. Perceptual organization directs the approach via grouping and the Gestalt principles, to segment the filtered point cloud and to later detect window patches. The evaluation of the approach displays a completeness of 95% and 92%, respectively, as well as a correctness of 95% and 96%, respectively, for the detection of rectangular and partially curved window frames in two big heterogeneous cluttered datasets. Moreover, most intrusions and protrusions cannot mislead the window detection approach. Several doors with glass parts and a number of parallel parts of the scaffolding are mistaken as windows when using the large-scale object detection approach due to their similar patterns with window frames. Sensitivity analysis of the input parameters demonstrates that the filter functionality depends on the radius of density calculation in the feature space. Furthermore, successfully employing the Gestalt principles in the detection of window frames is influenced by the width determination of window partitioning.

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Section: Introductionmentioning

confidence: 99%

Window Detection from UAS-Derived Photogrammetric Point Cloud Employing Density-Based Filtering and Perceptual Organization

et al. 2018

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“…Elberink and Vosselman, 2011;Jarzabek-Rychard and Karpina, 2016;Soudarissanane et al, 2008), in this research planar fitting and completeness of the generated 3DPCs were measured. For the former a planar object (e.g.…”

mentioning

confidence: 80%

Usability of aerial video footage for 3D-scene reconstruction and structural damage assessment

Cusicanqui¹,

Kerle²,

Nex³

2018

Preprint

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Abstract.Remote sensing has evolved into the most efficient approach to assess post-disaster structural damage, in extensively affected areas through the use of space-borne data. For smaller, and in particular, complex urban disaster scenes, multi-perspective aerial imagery obtained with Unmanned Aerial Vehicles and derived dense colour 3D-models are increasingly being used. These type of data allow the direct and automated recognition of damage-related features, supporting an effective post-disaster structural 5 damage assessment. However, the rapid collection and sharing of multi-perspective aerial imagery is still limited due to tight or lacking regulations and legal frameworks. A potential alternative is aerial video footage, typically acquired and shared by civil protection institutions or news media, and which tend to be the first type of airborne data available. Nevertheless, inherent artifacts and the lack of suitable processing means, have long limited its potential use in structural damage assessment and other post-disaster activities. In this research the usability of modern aerial video data was evaluated based on a comparative quality 10 and application analysis of video data and multi-perspective imagery (photos), and their derivative 3D point clouds created using current photogrammetric techniques. Additionally, the effects of external factors, such as topography and the presence of smoke and moving objects were determined by analyzing two different earthquake-affected sites: Tainan (Taiwan) and Pescara del Tronto (Italy). Results demonstrated similar usabilities for video and photos. This is shown by the short 2 cm of difference between the accuracies of video and photo-based 3D Point clouds. Despite the low video resolution, the usability of this data 15 was compensated by a small ground sampling distance. Instead of video characteristics, low quality and application resulted from non-data related factors, such as changes in the scene, lack of texture or moving objects. We conclude that current video data are not only more rapidly available than photos, but they also have a comparable ability to assist in image-based structural damage assessment and other post-disaster activities.

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“…Internal accuracy is related to the model precision, and different indicators can measure this 3DPC attribute. Based on previous methodologies (Oude Elberink and Vosselman, 2011;Jarzabek-Rychard and Karpina, 2016;Soudarissanane et al, 2008), in this research planar fitting and completeness of the generated 3DPCs were measured. For the former a planar object (e.g., roofs) was identified and extracted as 3DPC segments from every 3DPC and study area using CloudCompare (CloudCompare, 2017).…”

Section: Geometric Quality Assessmentmentioning

confidence: 99%

Usability of aerial video footage for 3-D scene reconstruction and structural damage assessment

Cusicanqui

Kerle

Nex

2018

Nat. Hazards Earth Syst. Sci.

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Abstract. Remote sensing has evolved into the most efficient approach to assess post-disaster structural damage, in extensively affected areas through the use of spaceborne data. For smaller, and in particular, complex urban disaster scenes, multi-perspective aerial imagery obtained with unmanned aerial vehicles and derived dense color 3-D models are increasingly being used. These type of data allow the direct and automated recognition of damage-related features, supporting an effective post-disaster structural damage assessment. However, the rapid collection and sharing of multi-perspective aerial imagery is still limited due to tight or lacking regulations and legal frameworks. A potential alternative is aerial video footage, which is typically acquired and shared by civil protection institutions or news media and which tends to be the first type of airborne data available. Nevertheless, inherent artifacts and the lack of suitable processing means have long limited its potential use in structural damage assessment and other post-disaster activities. In this research the usability of modern aerial video data was evaluated based on a comparative quality and application analysis of video data and multi-perspective imagery (photos), and their derivative 3-D point clouds created using current photogrammetric techniques. Additionally, the effects of external factors, such as topography and the presence of smoke and moving objects, were determined by analyzing two different earthquake-affected sites: Tainan (Taiwan) and Pescara del Tronto (Italy). Results demonstrated similar usabilities for video and photos. This is shown by the short 2 cm of difference between the accuracies of video-and photobased 3-D point clouds. Despite the low video resolution, the usability of these data was compensated for by a small ground sampling distance. Instead of video characteristics, low quality and application resulted from non-data-related factors, such as changes in the scene, lack of texture, or moving objects. We conclude that not only are current video data more rapidly available than photos, but they also have a comparable ability to assist in image-based structural damage assessment and other post-disaster activities.

show abstract

Quality Analysis on 3d Buidling Models Reconstructed From Uav Imagery

Cited by 8 publications

References 17 publications

Window Detection from UAS-Derived Photogrammetric Point Cloud Employing Density-Based Filtering and Perceptual Organization

Window Detection from UAS-Derived Photogrammetric Point Cloud Employing Density-Based Filtering and Perceptual Organization

Usability of aerial video footage for 3D-scene reconstruction and structural damage assessment

Usability of aerial video footage for 3-D scene reconstruction and structural damage assessment

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