Integration of Street Networks and LIDAR for
Modelling and Visualisation of Terrain Data
Martin Kada, Norbert Haala, Stephanie Maier, Dieter Fritsch
Institute for Photogrammetry (ifp)
University of Stuttgart, Germany
Geschwister-Scholl-Strasse 24D, D-70174 Stuttgart
E-mail: firstname.lastname@ifp.uni-stuttgart.de
ABSTRACT
The generation of realistic 3D landscape visualisations is already feasible, if data from airborne
laser scanning, which allows for a dense and accurate acquisition of the terrain surface, is com-bined
with additional textures from aerial images. While this is true for open areas, the presenta-tion
of more complex areas like urban landscapes requires additional processing of this original
data. As an example, during the visualisation of buildings from close virtual viewpoints, even
small geometric errors which can result from a non-planar triangulation of a building façade
may heavily disturb the degree of realism. Another problem is street surfaces, where the laser-based
point measurement is disturbed by objects like vehicles, traffic signs or trees. These erro-neous
points have to be eliminated by suitable filter algorithms, otherwise they will result in
disturbing height discontinuities in the street surface to be visualised. Within the approach pre-sented
in the paper, the required filter process is supported by the integration of existing street
networks. By these means a smoothed 3D shape of the longitudinal axis of the streets can be
estimated and expanded to the complete street region. In addition to demonstrating the DEM
filter process in street regions, the paper discusses its application to continuous level of detail
approaches which are commonly used in the real-time visualisation of such terrain models.
1. INTRODUCTION
Over the past years, applications based on the real-time visualisation of 3D urban landscapes
have spread considerably. Interactive virtual presentations of urban environments are used in
fields like tourism, architecture, city planning and marketing, or car navigation and location
based services. Frequently, the required data base is collected from airborne LIDAR. These sys-tems
provide information on the geometry of objects by densely distributed and accurate 3D
points. If they are combined with aerial cameras, object colour is additionally available. In prin-ciple,
the resulting surface description can be directly used for visualisation purposes. One prob-lem
is the huge amount of data; still this can be reduced efficiently by approaches based on the
generation of multiple levels of details. (Heckbert and Garland, 1997) give a comprehensive
overview on simplification algorithms that are applicable for such visualisation purposes.
For vertical surfaces, the limited spatial resolution of the LIDAR points from airborne data col-lection,
however, will frequently result in geometric errors due to interpolation effects. LIDAR
points relating to building objects need therefore be eliminated and replaced by 3D
building models. In addition, the processing of the airborne LIDAR data is also required to
eliminate other surface points, which do not correspond to the terrain surface.
Figure 1. 3D landscape model of Stuttgart rendered in a real-time visualisation environment.
The street in the front appears tilted and wavy because of the low resolution of the
sub-sampled LIDAR data.
Frequently, the terrain surface is occluded by trees or other vegetation. Especially in urban areas
small objects like pedestrians, vehicles or traffic signs additionally result in LIDAR points,
which do not represent the required terrain surface. Thus, a number of filter algorithms have
been developed, which are aiming on the extraction of the so-called bare-Earth surface from the
originally measured LIDAR points. An overview on recent approaches as a discussion of the
performance of the available filters is given in (Sithole and Vosselman, 2004). In summary, de-spite
of the reported considerable success of these algorithms, an absolutely error free classifica-tion
of these point clouds is not feasible, especially for difficult terrain like steep slopes and dis-continuities.
This mainly results from the complexity of automatic data interpretation, the some-times
low number of bare-Earth points and potential errors during point measurement.
Taking the aforementioned data processing, (Kada et al., 2003) show that literally a complete
city can be interactively displayed in 3D on today’s consumer PC systems (see Figure 1). In
order to improve rendering performance, speed-up techniques like continuous level of detail are
used for the terrain model whereas image based rendering techniques and generalisation is ap-plied
for the 3D building models. Especially for terrain visualisation, a multitude of algorithms
are available based on the triangle bintree (Lindstrom et al., 1996), quad-tree structured triangu-lation
(Roettger et al., 1998) or the progressive meshes (Hoppe 1998). All these algorithms have
in common that their underlying data structure is a sub-sampled, regular grid of height values
that are triangulated for rendering. Unfortunately, this simplification will destroy the features of
important objects. For example, streets are unlikely to maintain their horizontal position and
become tilted and wavy (see Figure 1).
One option to improve this simplification is the use of additional information. The work pre-sented
in this paper is especially focussed on the filtering of LIDAR data in street regions.
Within these regions LIDAR points measured on vehicles or traffic signs will lead to height dis-continuities
and bumps in the interpolated surface, which will considerably disturb the visual
impression. For this object type, a priori information on street networks is frequently available
and thus can be integrated to data processing (Kreimeike, 2004). The enhancement of street ob-jects
is another topic addressed in order to improve the visual perceptibility of street data.
Figure 2. DTM overlaid with the ATKIS street network.
