The paper is organized as follows: Section 2 gives an overview on
the proposed algorithm for generating 3D models from LIDAR
point clouds in three LOD. DTM generation from LIDAR data is
explained in section 3. Section 4 explains building outlines are
detected and approximated to produce second LOD. In section
5, the idea of modeling based on projecting 3D data into a 2D
plane and generating the LOD2 model is explained and finally, in
section 6, the achieved quality of the reconstructing buildings is
discussed.
2 PROPOSED ALGORITHM FOR 3D BUILDING
MODEL GENERATION IN THREE LEVELS OF
DETAIL
Figure 1 presents the proposed work flow for automatic genera
tion of building models. The process begins with separating non
ground from the ground regions by hierarchical filtering using
geodesic reconstruction. A DTM is produced by interpolating
the gaps obtained by the filtering process. The result represents
the first LOD, i.e. LODO. The approach continuous with extract
ing building regions from the ALS range data. A segmentation
and classification algorithm groups and classifies the laser range
pixels into building, vegetation and other classes. Next, the build
ing outlines are detected and approximated to reduce the number
of boundary pixels to some significant nodes. After estimating
an average height for the building, a prismatic building model is
generated to form the second LOD, i.e. LODI. Projection based
analysis of the LIDAR data is proposed for 3D building recon
struction to form the LOD2. The algorithm uses geodesic mor
phology for line detection and a 2D model driven technique for
building reconstruction.
3 AUTOMATIC DTM GENERATION - LODO
A hierarchical approach for filtering of the non-ground regions in
LIDAR data and generating digital terrain models has presented
in (Arefi and Hahn, 2005, Arefi et al., 2007b). Image recon
struction based on geodesic dilation is the core of this algorithm
which is proposed by (Vincent, 1993). The image reconstruc
tion is achieved by applying geodesic dilations until stability is
reached (Jahne et al., 1999). The idea of image reconstruction is
shown in figure 2. A profile of some non-ground objects located
on an uneven surface is shown in figure 2(a). Laser points (red
dots) are overlaid to the profile. The only input to generate im
age reconstruction is the height difference h shown in figure 2(b).
The result of geodesic image reconstruction is displayed in figure
2(c). The reconstructed image is subtracted from the original im-
(a) Profile representing the ground (black color) and the lo
cation of the laser points (red dots)
Mask
(b) mask and marker; marker = mask - h except pixels
at the boundary of the image when marker —mask
LIDAR DSM
Hierarchical filtering
of non-ground regions
DTM generation (LODO)
Building extraction
building outline
Determine main orientation
of building part
Localize building parts
T
Project 3D into 2D plane,
(d) nDSM of (c)
Figure 2: Geodesic image reconstruction by selecting a marker
image by subtracting h as offset value from the mask image.
age to shape the normalized DSM 2(d). In this initial nDSM or
nDSMO small hills may incorrectly be included. To avoid this
problem the initial nDSM of non-ground regions is evaluated by
a feature criterion that highlights jumps. The surface normal or
the local range variation (LRV) defined by the difference between
dilated and eroded LIDAR image in 3 x 3 local windows can be
used as features. Thresholding and connected components analy
sis leads to potential non-ground regions. The boundary pixels of
these regions are evaluated by these features and the regions with
height jumps are classified as non-ground. A sequence of marker
images, provided by different offsets h, are used hierarchically to
detect high as well as low non-ground objects. After separating
the ground and non-ground pixels in the LIDAR data the gaps are
filled using interpolation to get the DTM (LODO).