@article{gledocs_11858_9179,
author = {Dehbi, Youness and Henn, André and Gröger, Gerhard and Stroh, Viktor and Plümer, Lutz},
title = {Robust and fast reconstruction of complex roofs with active sampling from 3D point clouds},
year = {2020},

abstract = {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.},
note = { \url {http://resolver.sub.uni-goettingen.de/purl?gldocs-11858/9179}},
}