Full text: New perspectives to save cultural heritage

CIP A 2003 XIX"' International Symposium, 30 September - 04 October, 2003, Antalya, Turkey 
In this study, an inexpensive and robust 3D model acqui 
sition system is described. The system is more suitable 
for acquiring the 3D models of small artifacts such as such 
as cups, trinkets, jugs and statues. Image acquisition sys 
tem consists of a turn table, a digital camera and a com 
puter as shown in Figure 1. The system works as follows. 
The object to be modeled is placed on a turn table. By 
rotating the table, images of the object are acquired. This 
image sequence is then calibrated. Object silhouettes are 
segmented from the background. Each silhouette is back 
projected to an open cone volume in the 3D space. By in 
tersecting these open volumes, a coarse 3D model volume 
of the object is obtained. This coarse volume is further 
carved in order to get rid of excess volume on the concave 
parts. The surface appearance for model is also recovered 
from the acquired images. The model is considered as a 
surface composed of particles. Color of each particle is re 
covered from the images with an algorithm that computes 
the photo consistent color for each particle. The resultant 
appearance is stored in a texture map, while the shape is 
stored in a triangular mesh. Overall system diagram is il 
lustrated in Figure 2. 
Figure 2: Overall system diagram. 
The main advantage of proposed approach is its low com 
putational complexity. Most of the methods in the liter 
ature require a large amount of time and labor for recon 
struction. However, the complexities of our algorithms are 
reasonable. This enables building real time graphical user 
interfaces on top of these algorithms, by which real time 
interactive modifications can be done on the reconstructed 
models. This approach is very suitable for generating 3D 
models of small artifacts with high resolution geometry 
and surface appearance. Such artifacts may have handles, 
holes, concavities, cracks, etc. The proposed approach en 
ables robustly modeling of these properties also. Further 
more, we are currently developing algorithms for 3D mod 
eling from auto-calibrated images. The models obtained 
by this method are stored in Virtual Reality Modeling Lan 
guage (VRML) format which enables the transmission and 
publishing of the models easier. 
The organization of the paper is as follows: we first de 
scribe our camera calibration and geometry reconstruction 
processes in the following section. Section 3 gives the de 
tailed description of our appearance recovery algorithms. 
Results obtained in the framework of our study are given 
in Section 4, and the paper concludes with Section 5. 
In order to compute the parameters of the camera, we use 
a multi-image calibration approach (Miilayim and Atalay, 
2001). Our acquisition setup is made up of a rotary table 
with a fixed camera as shown in Figure 1. The rotation 
axis and distance from the camera center to this rotation 
axis remain the same during the turns of the table. Based 
on this idea, we have developed a vision based geometrical 
calibration algorithm for the rotary table (Miilayim et al., 
1999). Furthermore, we can compute very easily the dis 
tance between the rotation axis of the table with respect to 
the camera center which in fact facilitates the calculation 
of the bounding cube (Miilayim et ah, 2000). 
Once the bounding volume is obtained, carving this vol 
ume by making use of the silhouettes, a coarse model of 
the object is computed. This volume has some extra vox 
els which in fact should not exist. In this context, we have 
implemented a stereo correction algorithm which removes 
these extra voxels using photoconsistency (Miilayim and 
Atalay, 2001). Algorithm 1 which is mostly inspired from 
Matsumoto et. ah (Matsumoto et ah, 1999) outlines the 
Algorithm 1 Computing the photoconsistent voxels, 
reset all photoconsistency values of the voxels in V ob j ect 
to max photoconsistency value 
for all image i in the image sequence do 
for all visible voxels in image i do 
produce a ray from camera optic center 
find max photoconsistent voxel on the ray 
for all voxels between the max photoconsistent 
voxel and camera optic center do 
reduce voxel photoconsistency votes 
end for 
end for 
end for 
for all voxel v in voxel space V ob j ec t do 
if the photoconsisency of v is less than a threshold 
remove v from V ob j ec t 
end if 
end for 
In the algorithm, each voxel in the object voxel space V ob j ec t, 
starts with a high photoconsistency vote value; that is each 
voxel on the model generated by the silhouette based re 
construction is assumed to be on the real object surface. 
Each view i is then processed in the following manner. For 
each view i, rays from the camera center Ci through the 
voxels seen from that view i are traversed voxel by voxel. 
Each voxel on the ray is projected onto the images i — 1,

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