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Articles | Volume XLIII-B2-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 387–394, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-387-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 387–394, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-387-2022
 
30 May 2022
30 May 2022

AUTOMATIC EXTRACTION OF FACADES AND WINDOWS FROM MLS POINT CLOUDS USING VOXELSPACE AND VISIBILITY ANALYSIS

L. Hoegner1,2 and G. Gleixner1 L. Hoegner and G. Gleixner
  • 1Photogrammetry and Remote Sensing - TUM School of Engineering and Design - Technical University of Munich, Germany
  • 2Hochschule München University of Applied Sciences, Germany

Keywords: Mobile Mapping, Point Cloud, facade, Windows, 3D Object Extraction

Abstract. This contribution presents a method for extracting a 3D model of facades and windows from a point cloud. The point cloud is segmented based on a voxel octree, in which the facades are sought as planes. These can be used to filter out potential window points within the building, which are then analysed on their visibility by checking the occupancy grid of the voxel space. Here, methods of digital image processing are used for analysing both point clusters behind the facade and holes in the estimated facade planes as window candidates. Facades and windows are both simplified as rectangles. The test data set was gathered in a Mobile Laser Scanning campaign. While the segmentation fails in some cases, the extraction of facades and windows shows good results: 25 facades with 702 detected windows yield a detection rate of 86% with a false alarm rate of 13%. The reconstructed sizes of the windows differ from reference measurements in the range of centimetres to a few decimetres. These refined geometries can be used to enrich existing building models or for vehicle navigation without GNSS.