Fusion of Feature- and Area-Based Information for Urban Buildings Modeling from Aerial Imagery

Zebedin, Lukas; Bauer, Joachim; Karner, Konrad; Bischof, Horst

doi:10.1007/978-3-540-88693-8_64

Cited by 104 publications

(94 citation statements)

References 11 publications

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“…Interpolation leads to a much denser distribution of points such that not only better and more stable results can be obtained by using previously described methods but also image processing methods can be applied directly to the image that is given by the height map. This fits with a result of (Zebedin et al, 2008) that region growing leads to good results on dense height data obtained from areal images.…”

Section: Introductionsupporting

confidence: 89%

“…A further improvement would be to extend roof facets to areas outside the ground plan according to LiDAR data. On the other hand side, as shown in (Zebedin et al, 2008), instead of just applying a Ramer-DouglasPeucker-type algorithm to simplify polygons locally, it is pos- Figure 16: Trees around the left church lead to three small towers at corners of the building. Because of the same reason, the right church has an error at the left front side.…”

Section: Future Workmentioning

confidence: 99%

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Roof Reconstruction From Airborne Laser Scanning Data Based on Image Processing Methods

Goebbels¹,

Pohle-Fröhlich²

2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The paper presents a new data-driven approach to generate CityGML building models from airborne laser scanning data. The approach is based on image processing methods applied to an interpolated height map and avoids shortcomings of established methods for plane detection like Hough transform or RANSAC algorithms on point clouds. The improvement originates in an interpolation algorithm that generates a height map from sparse point cloud data by preserving ridge lines and step edges of roofs. Roof planes then are detected by clustering the height map's gradient angles, parameterizations of planes are estimated and used to filter out noise around ridge lines. On that basis, a raster representation of roof facets is generated. Then roof polygons are determined from region outlines, connected to a roof boundary graph, and simplified. Whereas the method is not limited to churches, the method's performance is primarily tested for church roofs of the German city of Krefeld because of their complexity. To eliminate inaccuracies of spires, contours of towers are detected additionally, and spires are rendered as solids of revolution. In our experiments, the new data-driven method lead to significantly better building models than the previously applied model-driven approach.

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Section: Introductionsupporting

confidence: 89%

Section: Future Workmentioning

confidence: 99%

Roof Reconstruction From Airborne Laser Scanning Data Based on Image Processing Methods

Goebbels¹,

Pohle-Fröhlich²

2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…In contrast, our algorithm works directly on raw 3D points without using highlevel geometric primitives, and is also more flexible with viewing angle (so long as parallax is observed). Results in Section 4 show that, without post processing, our pointbased algorithm achieves a comparable level of smoothness to the results reported in [30].…”

Section: Related Workmentioning

confidence: 61%

“…While a large number of existing approaches formulate 3D reconstruction as an MRF solved with graph cuts [23,10,15,29,30], MRF-based methods typically deal with a much smaller set of output 3D points (typically tens of thousands) and a smaller input image size (a few hundred by a few hundred). In our case, we generate an output dense cloud of many millions of 3D points, based on input images of a large size (1000 × 1000).…”

Section: Related Workmentioning

confidence: 99%

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Aerial 3D reconstruction with line-constrained dynamic programming

Liao

Lin

Medioni

2011

2011 International Conference on Computer Vision

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Aerial imagery of an urban environment is often characterized by significant occlusions, sharp edges, and textureless regions, leading to poor 3D reconstruction using conventional multi-view stereo methods. In this paper, we propose a novel approach to 3D reconstruction of urban areas from a set of uncalibrated aerial images. A very general structural prior is assumed that urban scenes consist mostly of planar surfaces oriented either in a horizontal or an arbitrary vertical orientation. In addition, most structural edges associated with such surfaces are also horizontal or vertical. These two assumptions provide powerful constraints on the underlying 3D geometry. The main contribution of this paper is to translate the two constraints on 3D structure into intra-image-column and inter-imagecolumn constraints, respectively, and to formulate the dense reconstruction as a 2-pass Dynamic Programming problem, which is solved in complete parallel on a GPU. The result is an accurate cloud of 3D dense points of the underlying urban scene. Our algorithm completes the reconstruction of 1M points with 160 available discrete height levels in under a hundred seconds. Results on multiple datasets show that we are capable of preserving a high level of structural detail and visual quality.

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Neural Procedural Reconstruction for Residential Buildings

Zeng

Furukawa

2018

Computer Vision – ECCV 2018

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Fusion of Feature- and Area-Based Information for Urban Buildings Modeling from Aerial Imagery

Cited by 104 publications

References 11 publications

Roof Reconstruction From Airborne Laser Scanning Data Based on Image Processing Methods

Roof Reconstruction From Airborne Laser Scanning Data Based on Image Processing Methods

Aerial 3D reconstruction with line-constrained dynamic programming

Neural Procedural Reconstruction for Residential Buildings

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