3. MODELLING 
3D modelling for both objects was based on point clouds 
combined from airborne and terrestrial laser scanning data. 
Both data sets were combined based on their common 
georeference — EPSG:2180 coordinate system. There were not 
performed any procedures designed to improve the matching of 
both sets. In case of need or necessity to improve the matching 
of the two considered data sets, the algorithms of transformation 
must be used, for example the method proposed in the work of 
Gruen and Akca (2005), which is based upon homologous 
surfaces matching with use of generalized least squares method. 
3.1 Geometry reconstruction 
Modelling was performed using the Cyclone Model 7.1 
software. The process of reconstruction a 3D vector model of 
building can be divided into the following stages (Borkowski et 
al., 2011): 
Scanning data approximation by planes. Due to ease the 
subsequent texturing, all the elements of the building and 
architectural details were approximated by planes or theirs 
compounds. The algorithm of growing regions was used to 
identify the various planes in the data set. 
Modelling the edges of the building. The edges of the building 
were modelled as a result of the intersection between 
neighbouring planes and were extended to the edges. 
Checking and correction the topology. This problem was, 
illustrated in Figure 3. It occurs most often in modelling roofs 
of buildings or other at least four neighbouring planes. Three 
roof planes define only two edges. The next roof plane defines 
additional two edges, which often do not intersect with the 
previous ones at one point. 
Building walls extending. Each wall which has the connection 
with the ground has to be extended to DTM. In this work DTM 
was created as a mesh from ground points of airborne laser 
scanning data set. Walls were extended to theirs intersections 
with DTM mesh. 
Final 3D vector model creating. After geometry reconstruction 
each building model was exported to DXF format. 
         
ight: correct topology. 
  
: incorrect topology. 
  
: Figure 
3.2 Texturing 
Texturing process consisted of imposing individual digital 
images of the same spatial resolution for each plane of 3D 
vector model. It was found that the size of one pixel on the 
object (wall) equal to Scm is sufficient for the correct 
visualization of the model, while created textures will not be too 
big, so the final model can be presented via the Internet in an 
effective way. Transformation and trimming digital photos to 
the individual planes of the 3D model with simultaneous 
resampling of images to the chosen spatial resolution is made in 
original software developed at the Institute of Geodesy and 
Geoinformatics. This software converts images using the 
projective transformation (there is need to select a minimum of 
4 homologous points on photo and model), and the resampling 
is being done using bilinear interpolation. For some textures, it 
was necessary to remove foreign objects such as cars or trees. 
This task was performed with use of the free software Gimp 2. 
Assignment and orientation of each to the appropriate model 
was performed in another free software - Google Sketchup 8. 
Visualization of the final models is shown in Figure 1. 
4. ACCURACY ASSESMENT 
4.1 Error sources 
Discussing the accuracy assessment of created 3D model first 
the errors that affect the final accuracy of the model have to be 
considered: 
1. The accuracy of TLS data. 
2. The accuracy of ALS data. 
3. Errors of integration of both data sets. 
4. Generalization of the model (level of detail 
modelling) and unambiguous identification of 
individual components of the model (modelled 
surface roughness). 
5. Errors resulting from the topology correction. 
6.  Texturing errors. 
The characteristics of both sensors show that the accuracy of the 
TLS data, both horizontal and vertical is at a few centimetres 
level and is about an order of magnitude better than the 
accuracy of the ALS data. Method of the two point clouds 
integration must therefore have an impact on the final accuracy 
of the model. In the presented work, integration was based on 
a simple combination of data sets. This approach is pragmatic 
and results from the assumptions made by the user of models. In 
Figure 2 there is shown that in practically all areas of the 
building there can be found points from both airborne and 
terrestrial scanning, but obviously with different resolutions. 
This fact was used to assess some kind of models internal 
accuracy, or evaluate the accuracy of the two point clouds 
matching. 
4.2 Internal accuracy 
   
  
Figure 4. ector model of the same building created from 
airborne (green) and terrestrial (grey) scanning data only.