Cl PA 2003 XIX th International Symposium, 30 September - 04 October, 2003, Antalya, Turkey
Figure 5: 3D point cloud generated with VirtuoZo automatic
matching on the metric images (ca 178 000 points).
3.2.2 Automated measurements with our software
A multi-photo geometrically constrained (MPGC) least squares
matching software package, developed at our Institute, was
applied to the metric images [Gruen et al., 2001, 2003]. The
automatic point measurement works according to the following
procedure:
1. Selection of one image as the master image. In our
application, the center image was selected;
2. Extraction of a very dense pattern of feature points in the
master image using the Foerstner operator;
3. Cross-correlation for each feature point to get the approxi
mate matches for the following matching procedure (using
also the epipolar geometry determined by phototriangula
tion);
4. MPGC matching for fine measurement, including patch re
shaping parameters. MPGC exploits a priori known geomet
ric information on orientation to constrain the solution and
allows for the simultaneous use more than two images
[Gruen, Baltsavias, 1988; Baltsavias, 1991].
In our application, for each feature point in the master image,
all 3 metric images were employed for matching. With the
MPGC approach, we can get sub-pixel accuracy matching re
sults and 3D object coordinates simultaneously (Figure 6, left)
and also, through covariance matrix computations, a good basis
for quality control.
Figure 6: The GUI of our MPGC matching software, with the
matching results and the computed 3D object coordinates (left).
The measured point cloud (right).
The procedure resulted in fairly reliable and precise matching
results. 49 333 points (without the surrounding rocks) and 73
640 points (with part of the surrounding rocks) were obtained.
The point cloud data is shown in Figure 6, right. Although we
use an automatic blunder and occlusion detection, some blun
ders are present in the final 3D point cloud. Moreover, there are
some gaps in the cloud, mainly due to the shading effects
caused by the variation of the illumination conditions during
the image acquisition. Furthermore, many folds of the dress
could not be reconstructed automatically, therefore these impor
tant small features had to be measured manually.
3.2.3 Manual Measurements
The dress of the Buddha is rich in folds, which are between 5
and 15 cm in width (Figure 7). The automated procedures could
not recover these small details, therefore only precise manual
measurements can reconstruct the exact shape and curvature of
the dress.
Figure 7: A closer view on the folds of the dress of the Buddha
(left) and how they were constructed (right).
We imported the metric images in the VirtuoZo stereo digitize
module [Virtuozo NT, 1999] and performed manual
stereoscopic measurements. Three stereo-models are set up and
points are measured along horizontal profiles of 20 cm
increment while the folds and the main edges are measured as
breaklines. With the manual measurement a point cloud of ca
76 000 points is obtained and the folds on the dress are now
well visible (Figure 8).
Figure 8: The point cloud of the manual measurement.
The main edges and the structures of the folds, measured as
breaklines, are well visible.
3.3 The modeling process
3.3.1 Automatic measurements
Due to the smoothness constraints and grid-point based
matching, in both automated procedures the small folds on the
body of the Buddha are not correctly reconstructed and the
