Gruen, Armin 
  
Details about the procedure can be found in Zhang, Baltsavias, 2000. This is an intermediate report, with the project still 
in progress. Results from straight line matching are shown in Figure 7. The fact that among the 460 matches only 5 are 
wrong is very encouraging and gives good prospects for the further development of the system. 
While manual intervention is foreseen and accepted in this procedure it is too early to argue about the required amount. 
A key point in our research will be the development of a metric for internal quality control. 
  
Figure 7. Result of straight line matching in a stereopair 
42  CC-Modeler 
CyberCity-Modeler, as the name suggests, was designed as a tool for data acquisition and structuring for 3-D city model 
generation. From the very beginning, CC-Modeler has been devised as a semi-automated procedure. This was done in 
view of the need to observe the following constraints: 
+ Extract not only buildings, but other objects as well, like traffic network, water, terrain, vegetation and the like 
+ Generate truly 3-D geometry and topology 
+ Integrate natural (real) image textures 
+ Allow for object attributation 
+ Keep level of detail flexible. Accept virtually any image scale 
+ Allow for a variety of accuracy levels (5 cm to 2 m) 
+ Produce structured data, compatible with major CAD and visualization software 
In site recording and modeling the tasks to be performed may be classified according to 
- Measurement 
- Structuring of data 
- Visualization, simulation, animation 
- Analysis 
In CC-Modeler the image interpretation and even the measurement task is done by the operator. The software does the 
structuring. For visualization, simulation, animation and analysis we largely resort to other parties', mostly commercial, 
software. 
Figure 8 describes the work- and dataflow of CC-Modeler. The operator measures on an Analytical Plotter or on a 
Digital Station in the stereomodel individual points that fully describe the visible part of an object, i.e. the roof of a 
building. The sequence of these points may be largely random. 
CC-Modeler presents a new method for fitting planar faces to the resulting 3-D point cloud. This face fitting is defined 
as a Consistent Labeling problem, which is solved by a special version of Probabilistic Relaxation. As an automatic 
topology generator, CC-Modeler is generic in the sense that any object, which is bounded by a polyhedral surface can 
  
International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Part B5. Amsterdam 2000. 315