%0 Journal article
%A Dehbi, Youness
%A Henn, André
%A Gröger, Gerhard
%A Stroh, Viktor
%A Plümer, Lutz
%T Robust and fast reconstruction of complex roofs with active sampling from 3D point clouds
%R 10.1111/tgis.12659
%R 10.23689/fidgeo-4833
%J Transactions in GIS

%X This article proposes a novel method for the 3D reconstruction of LoD2 buildings from LiDAR data. We propose an active sampling strategy which applies a cascade of filters focusing on promising samples at an early stage, thus avoiding the pitfalls of RANSAC-based approaches. Filters are based on prior knowledge represented by (nonparametric) density distributions. In our approach samples are pairs of surflets—3D points together with normal vectors derived from a plane approximation of their neighborhood. Surflet pairs provide parameters for model candidates such as azimuth, inclination and ridge height, as well as parameters estimating internal precision and consistency. This provides a ranking of roof model candidates and leads to a small number of promising hypotheses. Building footprints are derived in a preprocessing step using machine learning methods, in particular support vector machines.
%U http://resolver.sub.uni-goettingen.de/purl?gldocs-11858/9179
%~  FID GEO-LEO e-docs