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Cl PA 2003 XIX 11 ' International Symposium, 30 September - 04 October, 2003, Antalya, Turkey 
3.3.2 Manual measurements 
In the visualization of the point cloud of Figure 8 it is already 
possible to distinguish the shapes of the folds on the dress. 
Figure 11: Visualization in wireframe mode of the 3D 
structures on the central part of the dress of the Buddha 
reconstructed after the triangulation of the manual 
measurements 
This point cloud is not dense enough in some parts (except in 
the area of the folds) to generate a complete mesh with a 
commercial reverse engineering software. Therefore the 
generation of the surface is performed again with the 2.5D 
Delauney method, by dividing the measured point cloud in 
different parts. A mesh for each single point cloud is created 
and then all the surfaces are merged together with Geomagic 
Studio [www.geomagic.com]. The folds of the dress are now 
well reconstructed and modeled, as shown in Figure 11. The 
final 3D model, displayed in Figure 12, shows the completed 
reconstructed folds of the dress. Compared e.g. to Figure 9- 
upper, the new model represents a much better result. For 
photo-realistic visualization, the central image of the metric 
data set is then mapped onto the model, as shown in Figure 13. 
Figure 12: The shaded model of the Buddha, reconstructed 
with manual measurements on the three metric images. 
Figure 9: The triangulated shaded model (upper image) and 
the textured model automatically reconstructed with the 
commercial software. 
Then the central image of the metric data set is mapped onto the 
3D geometric surface to achieve a photo-realistic virtual model 
(Figure 9 and 10). 
Figure 10: Visualization of the shaded and textured model 
generated with our matching software on the metric images. 
point cloud of the statue or the surrounding rock looks very 
smooth. 
For the modeling, a 2.5D Delauney triangulation is performed 
and the final shaded model of the triangulated mesh is shown in 
Figure 9 and 10. The shaded models look a bit “bumpy“. This 
is due to small measurement errors and inconsistences in 
surface modeling. 
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