Full text: CMRT09

In: Stilla U, Rottensteiner F, Paparoditis N (Eds) CMRT09. IAPRS, Voi. XXXVIII, Part 3/W4 — Paris, France, 3-4 September, 2009 
Susanne Becker, Norbert Haala 
Institute for Photogrammetry, University of Stuttgart 
Geschwister-Scholl-Straße 24D, D-70174 Stuttgart 
KEY WORDS: Architecture, Point Cloud, Urban, LiDAR, Facade Interpretation 
The paper describes an approach for the quality dependent reconstruction of building facades using 3D point clouds from mobile 
terrestrial laser scanning and coarse building models. Due to changing viewing conditions such measurements frequently suffer from 
different point densities at the respective building facades. In order to support the automatic generation of facade structure in regions 
where no or only limited LiDAR measurements are available, a quality dependent processing is implemented. For this purpose, 
facades are reconstructed at areas of sufficient LiDAR point densities in a first processing step. Based on this reconstruction, rules 
are derived automatically, which together with the respective facade elements constitute a so-called facade grammar. This grammar 
holds all the information that is necessary to reconstruct facades in the style of the given building. Thus, it can be used as knowledge 
base in order to improve and complete facade reconstructions at areas of limited sensor data. Even for parts where no LiDAR 
measurements are available at all synthetic facade structures can be hypothesized providing detailed building geometry. 
Due to the growing need for visualization and modelling of 3D 
urban landscapes numerous tools for the area covering 
production of virtual city models were made available, which 
are usually based on 3D measurements from airborne stereo 
imagery or LiDAR. This airborne data collection, which mainly 
provides the outline and roof shape of buildings, is frequently 
complemented by terrestrial laser scanning (TLS). However, the 
applicability of standard TLS is usually limited to the 3D data 
capturing of smaller scenes from a limited number of static 
viewpoints. In contrast, the application of dynamic TLS from 
moving platforms allows the complete coverage of spatially 
complex urban environments from multiple viewpoints. One 
example of such a mobile mapping system, which combines 
terrestrial laser scanners with suitable sensors for direct 
georeferencing, is the StreetMapper system (Kremer and 
Hunter, 2007). This system enables the rapid and area covering 
measurement of dense 3D point clouds by integrating four 2D 
laser scanners with a high performance GNSS/inertial 
navigation system. By these means accuracy levels better than 
30mm have been demonstrated for point measurement in urban 
areas (Haala et al., 2008). 
In general, such systems allow for an efficient measurement of 
larger street sections including the facades of the neighbouring 
buildings. However, depending on the look angle during the 
scanning process, strong variations of the available point 
densities at the building facades can occur. Such viewpoint 
limitations and occlusions will subject the collected point cloud 
to significant changes of accuracy, coverage and amount of 
detail. For this reason, the following interpretation of the 
measured point clouds will be hampered by considerable 
changes in data quality. Thus, algorithms for automatic facade 
reconstruction have to be robust against potentially incomplete 
data sets of heterogeneous quality. For this purpose, dense point 
cloud measurements for facades with good visibility are used in 
our approach to extract rules on dominant or repetitive features 
as well as regularities. These rules then are used as knowledge 
base to generate facade structure for parts or buildings where no 
sensor data is available. By these means bottom-up and top- 
down propagation of knowledge can be combined in order to 
profit from both reconstruction techniques. The production 
rules, which are automatically inferred from well observed and 
modelled facades, are represented by a formal grammar. 
Such formal grammars are frequently used within knowledge 
based object reconstruction to ensure the plausibility and the 
topological correctness of the reconstructed object elements. 
Lindenmayer-systems (L-systems), which can be applied to 
model the growth processes of plants, are well known examples 
of formal grammars (Prusinkiewicz and Lindenmayer, 1990). 
So-called split grammars are introduced by Wonka et al. (2003) 
to automatically generate architectural structures from a 
database of rules and attributes. Similarly, Müller et al. (2006) 
present a procedural modelling approach for the generation of 
detailed building architecture in a predefined style. However, 
the variety of facade structures which can be generated is 
restricted to the knowledge base inherent in the grammar rules 
or model libraries. Thus, the appearance of facade elements is 
limited to prespecified types, even when leaving some freedom 
in the values of their parameters. Another problem while 
applying such approaches for object reconstruction is that 
manual interaction is required to constitute suitable building 
styles and translate them into some kind of model or grammar 
description. For this reason, several approaches aim at deriving 
such kind of knowledge from observed or given data. For 
example, Ripperda (2008) derives prior facade information 
from a set of facade images in order to support the stochastic 
modelling process. However, existing methods which try to 
derive procedural rules from given images as proposed by 
Müller et al. (2007) or Van Gool et al. (2007) still resort to 
semi-automatic methods. The same holds true for the work of 
Aliaga et al. (2007). They present an interactive system for both 
the creation of new buildings in the style of others and the 
modification of existing buildings. At first, the user manually 
subdivides a building into its basic external features. This 
segmentation is then employed to automatically infer a grammar

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