Window detection in sparse point clouds using indoor points

This paper describes an approach for detecting windows from multi-aspect airborne laser scanning point clouds which were recorded in a forward looking view. Since the resolution of the point cloud is much lower than from terrestrial laser scanning, new methods have to be developed to detect and, in a further step, reconstruct façade structures. The façade planes are detected using point normals and a regiongrowing algorithm. The approach for window detection uses the points which are lying behind the detected façades planes (indoor points). Regularities in the appearance of these points are of special interest to enable the detection of windows which are only weakly represented in the point cloud. Therefore it is checked with a Fourier Transform if a repetitive structure can be extracted. Otherwise peaks in the density of the indoor points are used to detect the windows. The approach is tested on data from four overflights over the area around the TU München. The tests show that windows having a repetitive structure can be detected well for larger façade parts which provide enough samples but the approach shows deficits for small façade parts and in the case of disturbing intrusions.