International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B-YF. Istanbul 2004 
  
  
Figure 6. Result of rooftops recognition 
Furthermore, in order to perform 3D modelling for the urban 
area, camera calibration for the first image and the last image 
were performed by combined adjustment (Chikatsu and Kunii, 
2002). Therefore, 3D data for the urban area could be calculated 
efficiently. 
Finally, 3D modelling for the urban area was performed by 
following procedure: (1) side surfaces for each building were 
constructed using the 3D data for the both ends of the matched 
line, (2) the recognized rooftops were put on the side surfaces. 
Figure 7 shows the 3D model for the urban area. 
8. CONCLUSION 
This paper investigates mainly 3 issues regarding 3D modelling 
for urban area using image sequences: (1) efficient and robust 
line matching method using optical flow and trifocal tensor, (2) 
performance evaluation of the proposal line matching, (3)more 
efficient epipolar matching, and followings are main results 
were obtained: 
* Line matching was improved by trifocal tensor. 
+ Proposal line matching method was efficiently more than 
other general methods. 
+ Efficient epipolar matching was performed by LSM. 
Thus, it is concluded that the line matching method comprised 
optical flow, trifocal tensor and epipolar matching is useful 
method for 3D modelling. However, there are still the following 
issues to be resolved before this method becomes operational. 
+ Recognition of Complicated rooftops. 
+ Texture mapping. 
  
(a) View from left side 
Acknowledgement 
The high vision imagery used in this paper were taken by Aero 
Asahi Corporation. The authors would like to thank ‘Aero Asahi 
Corporation. 
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(b) View from right side 
Figure 7. 3D model of urban area