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Planar roof surface segmentation using 3D vision


Internet search has initially been a strong driving force for the rapid emergence of 3D building models of large urban areas. Additionally, many commercial and governmental initiatives have been started to develop urban 3D geographic information systems in a transition from the classical 2D- to the novel 3D-GIS. The modeling of building roofs is thus a relevant research topic. The focus has been on the use of aerial LiDAR point clouds (Light Detection And Ranging). However, recent progress in digital aerial cameras has rendered possible the acquisition of very dense point clouds from high overlap digital aerial imagery, and to use these point clouds jointly with the image information to generate 3D building models. This paper presents a multi-step processing framework and work flow for the automatic segmentation of building roofs in densely built-up areas from high-resolution vertical aerial images. Details extruding from, or intruding into, a roof are being excluded so that each roof is being modeled by means of its planar segments and can then be classified as a specific roof type from a set of standard roof shapes. Our experimental work employs a test area in Graz (Austria) with 186 buildings.
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hal-01883108 , version 1 (03-10-2018)



Philipp Meixner, Franz Leberl, Mathieu Brédif. Planar roof surface segmentation using 3D vision. 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Nov 2011, Chicago, United States. pp.9 - 15, ⟨10.1145/2093973.2093976⟩. ⟨hal-01883108⟩


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