CANNING
chai
cluster point cloud
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tects roof ridges at
egmentation of the
dings using imagery
003).
raised for 3D roof
data directly. In the
dner and Forstner,
ia and Price, 2002;
, 2004) reconstruct
mination of model
;D roof point cloud
0 reconstruct roof
1any roof structures
The most common
l on the assumption
al plane segments.
rehensive study on
ing point cloud and
ion. In their work,
cloud according to
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, finally, the planes
sing the RANSAC
putation consumed,
eration of the seed
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. It is based on the
5 can be modeled
ar faces. Although
itries) reveal a hug
into two types: flat
from the initial step
cloud and consists
step, it is judged
flat roof; (ii) in the
first step, roof ridges are detected and segmented into line
segments; (iii) the points are identified and segmented so that
they are belonging to the plane patches which are intersected in
a line segment of a roof ridge; (iv) in the fourth step, the rest
points are clustered into several groups according to the
geometric distances among them. For each group, the
abovementioned process (from the initial step to the fourth step)
is carried out. The process terminates when there are only few
points or no point in the rest group.
The key novelty of this contribution is that the roof ridge is
detected at first and roof planes are extracted along their
corresponding roof ridges. This kind of approach makes the
search scale smaller while fitting the roof planes. At the same
time, the proposed approach follows the principle of bisection
method. In each iteration, the point cloud is divided into two
groups by the plane contains the roof ridge and vertical to the
ground plane. Whereby, point groups where fitting and
segmenting takes place are getting smaller along the iteration. In
a consequence, much computation cost is reduced.
The abovementioned process is implemented and tested on the
airborne laser scanning data provided by the ISPRS test project
on urban classification and 3D building reconstruction. Some
experimental results are shown in this work.
The rest of this paper is structured as follows. In the second
section, the study area and data used in the experiments are
described. Then the methodologies are explained in the third
section. Section 4 presents and discusses some experimental
results and gives some future works.
2. STUDY AREA AND DATA
The presented work is supposed to participate in the ISPRS test
project on urban classification and 3D building reconstruction.
In order to test the proposed algorithms, Airborne Laserscanner
(ALS) data in the area À of Vaihingen test data is selected,
since the roof structures in this area are more complicated in
comparison with those in other test areas.
According to the organization of the test project
(http://www.itc.nl/ISPRS WGIII4/tests datasets.html), the
Vaihingen test data set consists of 10 ALS strips acquired on 21
August 2008 by Leica Geosystems using a Leica ALS50 system
with 45° field of view and a mean flying height above ground of
500°m. The average strip overlap is 30%, and the median point
density is 6.7 points per square meter. The entire DGPF data set
4 points per square meter. The original point clouds were post-
processed by strip adjustment to correct for systematic errors in
georeferencing.
The points cloud represent all objects including buildings,
vegetation, water bodies, and other city facilities in Vaihingen.
At first, classification is conducted for these points by using
MicroStation. Then buildings are extracted and separated,
whereby buildings close to each other are treated as a big
building. The proposed algorithm starts from the point cloud of
an individual building.
3. METHODOLOGY
3.1 Detection and segmentation of roof ridges
In the building construction, a roof ridge is defined as the line
intersection at the top between the opposite slopes or sides of a
97
roof. Although non-flat building roofs (especially in European
countries) reveal a hug variety in structure, they could be one
type of the six primitive roofs (Figure 1), or might be a
combination of these primitives. Therefore, the roof ridge is one
line segment in case that a roof is a gabled, hipped, shed or
saltbox roof, while there are two intersected line segments in
case of a cross gabled or cross hipped roof. For a more
complicated roof structure, there will be three or even more line
segments which are intersected with a main ridge or in an “end
to end" form.
Coto
Gabled roof Cross gabled roof
rum
Hipped roof Cross hipped roof
Gr br
Saitbox roof Shed roof
Figure 1. six types of non-flat roofs
Inputting the unorganized laser scanning point cloud of a roof,
the process of detection and segmentation of roof ridges starts
from the step of extracting all the points that are located on the
roof ridges. In the proposed approach, the point cloud is
clustered into many (or w ) layers. In the ideal case, the points in
the highest layer represent then the roof ridge(s). But in some
cases the highest layer contains also points on the top of a
chimney, and points of error measurements. These points are
normally located with a distance to the roof ridge, while a point
on the roof ridge has shorter distance to its nearest
neighborhood. Therefore, they can be removed easily.
After the noise points are removed from the highest layer, all
the remaining points are then belonging to the roof ridge. They
will be projected on the XY-plane. Then they are segmented
into several line segments using the line-growth method as
following:
(a) All the points are sorted according to their x-value. The first
pa
. . B 0 .
point is taken as ‘! and a point P is found so that
port] zd, whereby d, is chosen as the shortest length of the
roof ridge in the test area.
p p?
(b) A line through '! and “2 is then calculated. During the
calculation, it is judged whether the line is parallel to x or y-axis.
(c) The points are selected, when they are located on the line or
very close to the line. The rest are treated as a group.
