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Title
New perspectives to save cultural heritage
Author
Altan, M. Orhan

CIPA 2003 XIX th International Symposium, 30 September - 04 October, 2003, Antalya, Turkey
464
camera (with each lens that should be used) once before
the campaign, unless it is subject to unexpected physical
impact.
Figure 1: Calibration target for camera calibration. The red
pyramids are the camera positions from an exemplary cal
ibration. In the center of the target one can see the marker
for automatic registration of the circles.
2.2 Data Capture
The actual recording consists of taking hand-held pictures
of the object, as it is nowadays done for any close-range
application. In order to provide sufficient redundancy for
reliable automatic reconstruction it is however necessary to
make sure that all relevant parts of the object are visible in
at least 3, better 4 images (rather than the theoretical min
imum of 2 for reconstruction from calibrated views). Fur
thermore we recommend an overlap of 80% (rather than
60%), which helps to ensure good image matching results
at no extra cost (except for longer computation times). Fig
ure 2 shows an example of a recording sequence captured
in Sagalassos.
3 RECONSTRUCTION
(b)
Figure 2: Recording for automatic reconstruction, (a)
Four images of a piece of the ’dancing girls frieze’ from
the Augustan ’Northwest Heroon’ in Sagalassos. (b) 3-
dimensional view of the recording setup.
3.1 Preprocessing
Before starting the actual reconstruction process lens dis
tortion is corrected, i.e. the images are warped to the ideal
pinhole camera geometry with the distortion parameters
determined through camera calibration. Furthermore the
user interactively has to segment the object to be modeled
from the rest of the image by digitizing its outline. The
segmentation is the only user interaction required for 3D
reconstruction apart from defining the sequence (see next
section) - it cannot be automated, because it is a decision
of the user, which cannot be derived from the scene ge
ometry alone. Note however that digitizing only needs a
few mouse-clicks, if it is assisted by the life-wire method,
as it is implemented in any image-processing toolkit (see
Figure 3.
3.2 Matching and Orientation
Before homologous points are extracted for image orien
tation, the images are partitioned into regular tiles to en
sure a good coverage of the object for orientation. We
use a partitioning of 8 x 6 tiles (which leads to roughly
squared tiles for standard cameras). A number of homol
ogous points is extracted for each tile with a hierarchical
area-based matcher (Klaus et al., 2002). The same points
are used for a set of 5 consecutive frames of the sequence to
achieve a stable image block. In our experiments we have
manually ordered the images (see Figure 4). In principle
it is possible to match all possible pairs of images and de
termine the ordering automatically using the homologous
points, if one is willing to accept the considerably higher
computational cost.
With the homologous points an initial estimate for the rel
ative orientations is sequentially recovered. Since the au
tomatically matched correspondences can contain a large
percentage of false matches (in some experiments up to
30%), it is necessary to use a robust estimation method.
The standard algorithm for the task is RANSAC (Torr and
Murray, 1997). Then the approximate 3D points are com
puted by forward intersection and the final orientations are
estimated via bundle adjustment with iterative outlier elim-