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The provision of these features on the computer controlling the 
scanning process is not really a technical problem since they are 
part of the processing software anyhow. Often, the processing 
software is needed at the home office for a previous project 
during a scanning campaign, however. Therefore, the licensing 
policy should provide those tools on both, the scanning and the 
office computer, even though only one license is bought. 
3. SOFTWARE FOR POINT CLOUD TREATMENT 
3.1 Visualization 
As a first processing tool, a viewer for the presentation of point 
clouds should be available, allowing spatial rotation and 
panning as well as zooming in and out. Since very large 
numbers of points may have to be handled, the performance of 
this tool should be optimized which includes point thinning (in 
the viewer only). Attribute dependent color coding (depending 
on observation point, distance, or reflectivity values) can 
support this first interpretation of the data. 
This tool, too, should be available without any extra license fee 
since it is needed in many cases by the clients to have a first 
possibility to review the scanning results. 
3.2 Data cleaning in single point clouds 
Since the single point measurements of a scanner are not 
individually supervised, there are several reasons for possible 
point recordings that do not represent the selected object 
surface. Among these are 
• reflections from objects in the background, 
• reflections originating in the space between scanner and 
object (trees and other objects in the foreground, moving 
persons or traffic, atmospheric effects such as dust or rain), 
• partial reflection only of the laser spot at edges, 
• multiple reflections of the beam, 
• range differences originating from systematic range errors 
caused by different reflectivity of the surface elements, 
• erroneous points caused by very bright objects (lights). 
Most of these wrong points can be eliminated best before 
several point clouds are registered. The elimination process has 
to be done interactively since no automatic method can foresee 
all possible constellations. Intelligent software features can 
assist in this process, however, and speed up this annoying 
editing process. 
Points in the fore- and background can easily be eliminated by 
the introduction of range limits (digitally or with the aid of 
graphical tools). Wrong points originating from multiple re 
flections can usually by caught with this method, too. 
More difficult to detect are wrong points at edges. Depending 
on the percentage of the laser spot reflected at the edge, the 
resulting offset may be large or very small. Since the effect is 
systematic (e.g. for triangulation scanners the wrong point can 
always be found on the extension of the beam from scanner to 
edge, Boehler et. al., 2001), the software should search for such 
candidates, highlight them, and leave it to the operator to keep 
or delete them. 
Some tests indicate that certain scanners show range deviations 
depending on the reflectivity of the surface material. It should 
be examined if these errors can be modeled accurately enough 
to make an automatic correction process possible. 
3.3 Data filtering and point thinning 
In addition to the gross errors described in the previous section, 
the scanning results will show a certain noise due to the limited 
accuracy of the measured elements. If the object part is known 
to be smooth, the application of a low pass or median filter can 
improve the situation considerably. It should be noted, however, 
that filtering will influence all object parts in the same way. If 
the object consists of smooth parts as well as of edges, filtering 
may not be advisable at this point of the process. 
Point thinning can have a similar effect as filtering if those 
points having large deviations from an intermediate surface are 
preferably deleted. If several scans were taken from different 
observation stations, it may be advisable to wait with point 
thinning until all measurements are combined in the registration 
process (see below, 3.6). 
3.4 Registration 
Registration is needed for two different purposes: 
• Combination of several point clouds taken from different 
observation points or 
• referencing the object in a global coordinate system. 
Tie and control points which are recognizable in the point cloud 
are needed to accomplish this task. These points may just be 
features of the object (e. g. comers) or special targets (spheres, 
flat targets with high reflectivity) at carefully chosen locations. 
In the case of global coordinates, these targets have to be sur 
veyed by geodetic methods (tacheometric polar surveys or 
sections) or close range photogrammetry (bundle adjustment). 
In any case, a sufficient number of known points (three, better 
four) in a point cloud will yield better results than just stitching 
the clouds together with tie points. On the other hand, if overlap 
is large enough, many surface points are available to give 
enough geometrical strength to a solution using surface con 
ditions only. In any case, it should be considered that error 
propagation may be unfavorable for long objects and inner or 
outer closed surfaces consisting of many single scans. 
Since objects, conditions and accuracy demands may differ very 
much from case to case, an ideal registration software should 
allow both, registration by special targets and by overlapping 
point clouds (or a combination of both). Rigid least squares 
adjustment should be available for either task. 
3.5 Data cleaning in registered point clouds 
Some wrong points may have survived the previous cleaning 
process (3.2) or can definitively be interpreted as errors after 
registration only. Those have to be eliminated now, at this stage 
of the process. 
3.6 Point thinning after registration 
Due to overlapping areas where points are abundant, but also 
due to different distances from the scanner and different angles 
between the laser beam and the surface normal, point density 
will be differing to a great extent within the object. A good 
point thinning software should not only consider an even point 
distribution with respect to the surface, but should also select 
those points for deletion which have poorer accuracy due to 
poor incidence angles or due to larger distances from the 
scanner (which is of special importance in the case triangulation 
scanners).