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Title
New perspectives to save cultural heritage
Author
Altan, M. Orhan

CIP A 2003 XIX th International Symposium, 30 September - 04 October, 2003, Antalya, Turkey
491
scanning,
ural heritage
ect. In the
the model
clouds that
advantages
act sections
cal analysis
to create a
i
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Italy
that has
geometry
without
)me very
complex if the surveyed object has a large number of
discontinuities (for example, a historical façade with
columns, eaves, statues and niches on the façade, etc.).
In this case the only way to be able to carry out a correct
solid modelling of the object is to use segmentation
techniques. By segmentation we mean the extraction of
geometric information from a set of data and their
partitioning into uniform entities. Each of these entities will
then be modelled separately. The final form of the model of
the object will be generated from the set of the various
portions that have been separately modelled.
2. SEGMENTATION TECHNIQUES
2.1 Manual segmentation
As previously shown, in order to carry out a solid modelling
starting from a three-dimensional point cloud, it is necessary
to segment the object into its simplest portions. Modelling
software usually allows this type of operation to be carried
out in a completely manual way. The operator chooses and
selects a portion of a point cloud on the screen and models
this portion of the points to create meshes or nurbs.
On other occasions it is possible to manually segment the
objects using graphic management software such as
AutoCAD. Therefore the objects are segmented and some
portions of the object are placed on different layers.
This type of segmentation which, according to what has been
described can be defined as manual requires a remarkable
effort by the operator to identify the portions of the points.
This is even more the case when the model is dense with
information and complex in form.
Today an increasing effort is being made in the field of
research into the implementation of automatic segmentation
techniques to resolve this problem.
2.2 Automatic segmentation
The problem of segmentation can be found in many scientific
applications.
As far as laser technology is concerned, there are many
researchers who at present are working on comparisons of
segmentations. These are usually carried out on laser data
derived from aerial platforms. Some very interesting results
have already been obtained.
The case of aerial scanning differs however from that of
ground laser scanning. The laser data from aerial platforms
are distributed in planimetry while the most obvious
discontinuities can be found at heights.
In the case of ground laser scanning, the data are widely and
differently positioned in space and the complexity of these
dispersions depends above all on the complexity of the
surveyed object.
Although the field of aerial laser scanning is rather different
from the field of ground laser scanning (close range), some of
the segmentation algorithms and techniques that were
developed for segmentation of DDEMs obtained from aerial
platforms can easily be applied or adapted for ground
surveying.
2.3 Segmentation through the identification of the main
plains of the object
In order to be able to model the point clouds acquired with
ground type laser scanners it is first of all necessary to divide
the 3D model into its main plains. In the case of a building,
the main plains can be represented by the individual facades
or, as is the case for more complex buildings, by portions of
these facades.
In order to be able to segment the object according to its main
plains, the authors have developed specific software which
functions as follows:
A point that belongs to the scansion. This point can be
imposed by the key board or by the operator, or can
even be extracted by chance.
A search radius around an initial point is established
and all the points that fall into to this area are
considered. The set of these points is used to determine
a mean plain that passes through the points. This plain
is calculated using the least square estimation.
If the parameters of the estimation that derive from the
calculation of the mean plain that passes through the
chosen points are considered to be sufficiently correct
(this decision is up to the operator), or in other words if
the maximum rejects obtained by the estimation are
lower than a previously imposed value, it can be
considered that one of the main plains has been
identified. After having identified one of the main
plains, all the points of the scansion that are at a closer
distance from the plain, according to a previously
established reference, can be considered as belonging to
the plain. It is the operator who decides the maximum
distance between the points and the plain for the
aggregation.
o
o
o
• First aggregation point
® Aggregated points
© Non aggregated points
The mean plane
Figure 2 - An example of a plain considered valid, as seen
from above.
In the case in which the parameters of the estimation of
the plain are not considered valid, that is, when the
number of rejects is too high, they are considered as not
belonging to a plain that can be considered as being a
main plain.
The aggregation operation is continued. Another point
to which a layer has not yet been assigned is extracted
and the operation is repeated.
All the procedures that have been described are repeated
until all the points of the scansion have been assigned a layer
or until a maximum number of plains, as imposed by the
operator, have been found.
The described procedure is nothing else but a first very
rough segmentation, but it allows one to easily separate, for
example, two facades of the same building that are oriented
in a different way.