Abstract
Abstract. With multi-looking oblique view airborne laser scanning (ALS) it is possible to create point clouds with a nearly complete 3D coverage of a larger area. This allows, in contrast to nadir view ALS, the extraction of façade information. This paper concentrates on the reconstruction of windows. Because of the limited point density, received from oblique view ALS, the approach aims at the reconstruction of rectangular windows from sparse point clouds (<10 points/ m2). In a pre-processing step window centres are determined. For that indoor points, which lie behind the façades planes, are detected. The following reconstruction process consists of two main steps. First the window centres are used to create a hypothesis for the window outline by searching for a rectangle with maximum size, which includes the window centre but no points of the point cloud. In the second step these outlines are represented by probability density functions to model the uncertainty of the edges. All edges of one type, i.e. left, right, upper or lower edge, are combined by multiplication of their functions. Subsequently these functions are used to allocate the final edge positions to each window. The windows can be reconstructed with a width and height error of a few decimetres, what corresponds to the typical point distance in the point cloud, as far as the window centres are provided in a sufficient quality. The approach performs better the more equal windows are arranged in a façade row or column.
Highlights
1.1 MotivationToday most of the larger cities have got a 3D building model in LoD2, with modelled roof structures and textured façade faces
The distances of the edges from the quadrant parts which are used for the weighting are multiplied with the factor 2 before they are used as factor b for the calculation of the probability density function (PDF)
This paper has shown a possible approach for the reconstruction of rectangular windows based on two main steps: First a hypothesis for the outline is created with an iterative quadrant based search algorithm, which functionality depends on the quality of the provided window centres
Summary
1.1 MotivationToday most of the larger cities have got a 3D building model in LoD2, with modelled roof structures and textured façade faces. Beside a more realistic visualization for navigation or urban planning these façade elements play a role in applications like thermal inspection of buildings (Iwaszczuk et al 2011), since the window area shows the temperature of a reflected area instead of the building temperature, or the analysis of Persistent Scatterers in radar images (Auer et al 2010) Another large subject area, where detailed building models are used, is the field of construction site monitoring and building information models (Tang et al 2010). A point cloud with nearly complete 3D coverage of a larger area, but with limited point density, can be created in that way From this the question arises if this kind of data is sufficient to derive façade details, at least the most important ones, especially the windows. Addressing this question an approach is introduced in this paper for the automatic detection and reconstruction of rectangular windows for this class of data
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