stanbul 2004 
the most 
ofs, hip 
Is is fixed, 
ameters will 
orientation 
arbitrary 
estricted to 
uildings 
using a 
terized 
hat the 
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complete the 
uction stage, 
and possible 
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1 the case of 
complete the 
y means of a 
x is the one 
ility can be 
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(m| x, e) can 
.[ S.Scholze 
r the plane. 
ibution with 
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B3. Istanbul 2004 
  
P(m| x, e) « 
0, 
1 5 | 4 2 
exp(—-— (Lt. — x,) z,)d0 
Mg, Ann yt T 2 22 ! A 
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When a model primitive type is choosed , the plane number 
is determined and the only unknown parameter is 0 . To set up 
plane hypothses ,reconstructed 3D line segments in the patch 
neighbourhood are considered.The DSM could be used to 
determined the initial angle range 1 0,,,0,,. j.The max 
posterior model probabilities be choosed as the estimated 
parameter value. 
3.6 3D Representation 
When all buildings are successfully reconstructed, we can 
provide a simple back-projection of the reconstructed 3D 
building to the aerial image. The corresponding image patch for 
each visible plane of a building is available and can be used for 
texture mapping. 
A 3-D visualization tool was developed for displaying the 
generated building models with level of detail models. 
4. RESULT 
Reconstructed buildings are merged into textured ground 
generated from DSM and orthoimages. These 3D models are 
visualized realistically as shown in Figure 6. 
  
Figure 6 . Experiments result 
5. CONCLUTION 
This paper describes an algorithm framework for building 
recognition in urban area with laser scanned data (LIDAR) and 
image sequence. With the framework presented in this paper we 
can construct 3D urban models in a more efficient and reliable 
way. 
ACKNOWLEDGEMENTS 
Thanks for the supporting from MapMaster (Japan) with all 
data. 
583 
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