Detection of Small Roof Details in Image Sequences

Bulatov, Dimitri; Pohl, Melanie

doi:10.1007/978-3-642-38886-6_56

Cited by 5 publications

(4 citation statements)

References 20 publications

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“…Once the building point cloud is extracted, the next step is the 3D building modelling. In this context, Bulatov and Pohl (2013) developed an approach for detecting the small roof details in image sequences. Assumptions were applied in order to tighten the search range for chimneys and to reduce the number of false alarms.…”

Section: Related Workmentioning

confidence: 99%

Automatic evaluation and improvement of roof segments for modelling missing details using Lidar data

Kurdi

Awrangjeb

2020

International Journal of Remote Sensing

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Despite the large number of studies conducted during the last three decades concerning 3D building modelling starting from Lidar data, two persistent problems still exist. The first one is the absence of some roof details which will not only disappear in the building roof model due to their small areas regarding the point density, but are also considered as undesirable noise among the modelling procedures. The second problem consists in that the involved segmentation algorithms do not perform well in the presence of noise in the building point cloud data. These two problems generate undesirable deformation in the final 3D building model. This paper proposes a new automatic approach for detecting and modelling the missing roof details in addition to improving the building roof segments. In this context, the error map matrix, which presents the deviations of points to their fitting planes, is considered. Moreover, this matrix is analyzed in order to deduce the mask of missing roof details. At this stage, a new numeric factor is defined for estimating the roof segmentation accuracy in addition to the validity of the roof segmentation result. Then, the building point cloud is enhanced in order to decrease the negative noise influence and, consequently, to improve the building roof segments. Finally, the functionality and the accuracy of the proposed approach are tested and discussed.

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Section: Related Workmentioning

confidence: 99%

Automatic evaluation and improvement of roof segments for modelling missing details using Lidar data

Kurdi

Awrangjeb

2020

International Journal of Remote Sensing

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“…Moreover the evaluation of local height differences allows a further subdivision of larger building complexes. Additional or alternative ways to reduce the set of points is to detect small roof segments (such as chimneys, see [22]) in advance and then subdivide building complexes along their diagonals (see [23]) once building ground polygon has been obtained. The obtained sub-clouds represent roof structure of buildings and comprise the main input for the subsequent vectorization.…”

Section: Extraction Of Semantic Models From Elevation Mapsmentioning

confidence: 99%

Fast and Robust Generation of Semantic Urban Terrain Models from UAV Video Streams

Rothermel

Haala

Wenzel

et al. 2014

2014 22nd International Conference on Pattern Recognition

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We present an algorithm for extracting Level of Detail 2 (LOD2) building models from video streams captured by Unmaned Aerial Vehicles (UAVs). Typically, such imagery is of limited radiometric quality but the surface is captured with large redundancy. The first contribution of this paper is a novel algorithm exploiting this redundancy for precise depth computation. This is realized by fusing consistent depth estimations across single stereo models and generating a 2.5D elevation map from the resulting point clouds. Disparity maps are derived by a coarse-to-fine Semi-Global-Matching (SGM) method performing well on noisy imagery. The second contribution concerns a challenging step of the context-based urban terrain modeling: Dominant planes extraction for building reconstruction. Because of noisy data and complicated roof structures, both dominant plane parameters and initial values for support sets of planes are obtained by the J-Linkage algorithm. An improved pointto-plane labeling is presented to encourage the assignment of proximate points to the same plane. This is accomplished by non-local, Markov Random Field (MRF) -based optimization and segmentation of color information. The potential and the limitations of the proposed methods are shown using an UAV video sequence of limited radiometric quality.

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“…The algorithm is described in a detailed way in Ref. 10. First, small elevated regions of the DEM are found by applying a hot-spot detector, such as the MSER-operator.…”

Section: Small Elevated Objectsmentioning

confidence: 99%

“…More detailed results are given in the original paper. 10 We consider only a small number of airborne images in which most of the walls are hidden by other buildings and trees or just have an insufficient resolution because of the Nadir view. So, we only consider the rooftops of the buildings for reconstruction and refinement.…”

Section: 5d Datamentioning

confidence: 99%

Extraction and refinement of building faces in 3D point clouds

2013

Self Cite

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In this paper, we present an approach to generate a 3D model of an urban scene out of sensor data. The first milestone on that way is to classify the sensor data into the main parts of a scene, such as ground, vegetation, buildings and their outlines. This has already been accomplished within our previous work. Now, we propose a four-step algorithm to model the building structure, which is assumed to consist of several dominant planes. First, we extract small elevated objects, like chimneys, using a hot-spot detector and handle the detected regions separately. In order to model the variety of roof structures precisely, we split up complex building blocks into parts. Two different approaches are used: To act on the assumption of underlying 2D ground polygons, we use geometric methods to divide them into sub-polygons. Without polygons, we use morphological operations and segmentation methods. In the third step, extraction of dominant planes takes place, by using either RANSAC or J-linkage algorithm. They operate on point clouds of sufficient confidence within the previously separated building parts and give robust results even with noisy, outlier-rich data. Last, we refine the previously determined plane parameters using geometric relations of the building faces. Due to noise, these expected properties of roofs and walls are not fulfilled. Hence, we enforce them as hard constraints and use the previously extracted plane parameters as initial values for an optimization method. To test the proposed workflow, we use both several data sets, including noisy data from depth maps and data computed by laser scanning

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Detection of Small Roof Details in Image Sequences

Cited by 5 publications

References 20 publications

Automatic evaluation and improvement of roof segments for modelling missing details using Lidar data

Automatic evaluation and improvement of roof segments for modelling missing details using Lidar data

Fast and Robust Generation of Semantic Urban Terrain Models from UAV Video Streams

Extraction and refinement of building faces in 3D point clouds

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