International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part BS. Istanbul 2004 
  
0.58 mm 
   
0.44 mm 
  
AZ 0.36 mm 
  
  
  
Tab. 4: Average deviations between calculated object points 
and reference points 
Further bundle adjustments were calculated using panorama 
imagery of an inner courtyard within the campus of the Dresden 
University. Fig. 11 shows one of 4 panoramas. This courtyard 
has a dimension of approximately 45 x 45 m in the ground view 
and the building height is ca. 20 m. 
  
Fig. 11: Panorama of an inner courtyard within the campus of 
Dresden University 
A first calculation was carried out using image coordinates of 
120 signalized points, which could be measured semi- 
automatically with subpixel precision. The free network 
adjustment resulted in Go = 0.24 Pixel. The mean standard 
deviation of object points is summarized in the following table. 
  
  
  
Signalized points 
Go [pixel] (120 sign. points) 0.24 
Ox [mm] (120 sign. points) 2.6 
9y [mm] (120 sign. points) 2.5 
9z [mm] (120 sign. points) 2.8 
  
  
  
  
Tab. 5: Results of panoramic bundle block adjustment of 
signalized points of a real-word object 
The same dataset was processed with 48 additional natural 
object points, which were measured manually. Table 6 shows 
the mean standard deviations of these natural points. 
  
  
  
  
  
Natural points 
Gy [pixel] (all 168 points) 0.41 
Ox [mm] (48 natural points) 4.2 
Gy [mm] (48 natural points) 4.7 
az [mm] (48 natural points) 53 
  
  
Tab. 6: Results of panoramic bundle block adjustment of 
natural points of a real-word object 
4.2 Object model generation 
After the calculation of coordinates of points representing the 
object geometry such as edges or corners via bundle block 
adjustment or spatial intersection, it is possible to generate 3D- 
models of the 360?-surrounding. After creating surfaces using 
these discrete points, the model can be filled with high- 
resolution texture from the panoramic images. This texture 
mapping can be achieved by projecting image data of an 
orientated and calibrated panorama onto the object surface 
planes using the accurate mathematical model as described in 
chapter 3. Fig. 12 illustrates the principle of this projection. 
N 
  
Fig. 12: Principle of the projection of panoramic texture 
into a 3D-Model 
Using the 3D object geometry as outcome of the geometric 
processing of panoramic imagery it is then possible to generate 
precise photo-realistic 3D-models of objects such as city 
squares, rooms or courtyards, e.g. with CAD-software (Fig. 13). 
Some virtual reality models (VRML) can be found in 
(Schneider, 2004). 
  
Fig. 13: 3D-modelling with AutoCAD using object geometry 
and texture from panoramic imagery 
4.3 Epipolar line geometry 
The developed mathematical model was further used to 
describe the epipolar line geometry for panoramic images. As 
evident from Fig. 14, in most cases the epipolar lines are 
actually no straight lines but rather epipolar curves in the 
image. 
  
  
p 
Fig. 14: Epipolar line geometry of panoramas 
    
  
  
  
   
   
  
   
  
  
  
  
  
  
   
  
   
      
   
  
  
   
   
   
  
  
   
  
   
   
   
  
  
  
   
    
   
  
  
   
   
  
  
  
  
  
    
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