Full text: Proceedings, XXth congress (Part 3)

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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B3. Istanbul 2004 
change the volume and the outline of the model. From the 
traditional photogrammetric point of view, the floating model is 
an extension of the floating mark. Instead, it is not only floated 
in the object space, but also deformable to fit the outline of the 
object. From the model-based building extraction point of view, 
floating mark is an exceptional case of floating model without 
any shape parameter. The details of floating models are given in 
the next chapter. 
Comparing to other modelling schemes, such as polyhedral 
models, prismatic models, or parameterized polyhedral models, 
CSG models scheme do not use one complicated model for a 
whole building, but a combination of several volumetric 
primitives. À primitive is a predefined simple solid model 
which is associated with a number of parameters representing 
the shape and pose. The final complete model is composed of a 
number of primitives, following the Boolean set operation. 
These characteristics make CSG models flexible enough for 
modelling most of the buildings, yet still can be simply 
described by a small set of rules. Therefore, CSG modeling 
scheme is suggested for model-based building extraction by 
many pioneers (Braun et al., 1995, Giilch ef al., 1998, Veldhuis, 
1998, Ermes ef al., 1999, Tangelder et al., 1999). The proposed 
floating model idea complies with the CSG principle. Each kind 
of floating models is a primitive. A building may be modelled 
by a combination of various floating models. 
Model-based building extraction (Sester and Fôrstner, 1989, 
Vosselman, 1998, Brenner, 1999, Fischer er al., 1999, Ameri, 
2000, Suveg and Vosselman, 2000, Tseng and Wang, 2003) 
starts with hypotheses of building model representing a 
specified target on the scene, and verifies the compatibility 
between the model and the existing image data. Approaches to 
model-based building extraction are mostly implemented in a 
semi-automatic manner, solving the model-image fitting 
problem based on some high-level information given by the 
operator. The spatial data of a building object are determined, 
when model-image fitting is achieved optimally. Therefore, the 
key is the algorithm that is able to determine the pose and shape 
parameters of a floating model such that the edge lines of the 
wire-frame, as projected into the images, are optimally 
coincided with the corresponding edge pixels. It is assumed that 
the image orientations are known and the pose and shape 
parameters are approximately determined through an interactive 
process. To deal with this problem, we proposed a tailored 
least-squares model-image fitting algorithm in chapter 3 as a 
major component of the building extraction framework. 
The proposed semi-automated strategy is shown as figure |. 
The basic idea is based on “Human is good at interpretation, 
computer is good at calculation.” Therefore, the high-level tasks, 
such as primitive selection, approximately fitting, Boolean set 
operation, and local modification, are accomplished by operator. 
While the low-level task, such as complicated calculation of 
optimally fitting, is accomplished by computer programs. 
    
  
   
   
Manual Operations 
Model Base © 
Ü 
    
  
T Em 
   
CSG Boolean 
  
    
Optimal 
       
      
      
  
  
Primitive Interactive Operation 
n eti -» Approximate HP] Model-image and 
Pc es Fitting Fitting Local. 
Modification 
  
  
  
   
    
  
  
  
  
  
  
  
Figure 1. The proposed semi-automated strategy for model- 
based building extraction. 
   
To test the practicabilitv and to evaluate the accuracy, we 
designed a computer program for implementing the proposed 
procedures. Ten various buildings around the NCKU campus 
within four overlapped aerial images were selected as the 
experimental objects. Results and accuracy assessment are 
given in chapter 4, which shows the practical capability and 
potential. 
2. BUILDING MODELLING 
Building modelling and model-image fitting are two major 
issues in model-based building extraction. The issue on building 
modelling is how to establish a set of representative and 
complete building models. Buildings in Taiwan are highly 
diverse in appearance. lt is almost impossible to categorize 
them into distinct styles of representative models. However, 
there is still regularity inherent in the most building structure, 
which allows describing buildings with a small set of rules. 
Since the buildings are basically volumetric objects, it is 
adequate to modelling by 3D solid entities. By summarizing the 
experiences of measuring by floating mark and reviewing the 
references of line photogrammetry and CAD-based 
photogrammetry, a naval measuring tool of “floating model” is 
proposed. 
2.1 Floating Models 
Traditional photogrammetric mapping systems concentrate on 
the accurate measurement of points. The floating mark is a 
simple way to represent the position of a point in the space, and 
thus, has been served as the only measuring tool on the stereo 
plotters up to nowadays. The idea behinds the floating mark is 
to depict the intersection V, of the bundle from the projection 
centers O, and O,, through the image point v;' and v;" to the 
ground, as figure 2 shows. If the conjugate point v;' or v,” 
moves along the epipolar line, the intersection point V; 
represented by floating mark will raise or sink along the bundle, 
seems like "floating" in the object space. This simple 
representation of a 3D coordinates has been very useful for 
photogrammetric measurement and 2.5D mapping system. 
However, the floating mark reaches its limits when the 
conjugate points can not be identified due to the occlusions or 
interferences from other noises. And with the increasing needs 
of 3D object models, point-by-point measurement has been 
become the bottleneck of the production. 
0, #0, 
Figure 2. The concept of floating model. 
Line photogrammetry takes the nature of 3D linear feature into 
consideration and introduces the 3D line equations as unknowns 
instead of 3D coordinates. Take figure 2 for example, operator 
  
   
    
   
  
   
  
   
  
  
  
   
   
   
   
  
   
    
      
   
   
  
  
     
    
    
   
   
   
  
  
  
  
   
    
   
   
  
  
   
  
   
   
  
  
   
  
  
   
   
   
  
  
   
   
  
  
   
    
  
  
  
   
   
	        
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