TY  - JOUR
A1  - Dehbi, Youness
A1  - Henn, André
A1  - Gröger, Gerhard
A1  - Stroh, Viktor
A1  - Plümer, Lutz
T1  - Robust and fast reconstruction of complex roofs with active sampling from 3D point clouds
Y1  - 2020
JF  - Transactions in GIS
DO  - 10.1111/tgis.12659
DO  - 10.23689/fidgeo-4833
N2  - 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.
UR  - http://resolver.sub.uni-goettingen.de/purl?gldocs-11858/9179
ER  -