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New perspectives to save cultural heritage
Altan, M. Orhan

Ula§ Yilmaz, Oguz Ôzün, Burçak Otlu, Adem Mulayim, Volkan Atalay
{ulas, oguz, burcak, adem, volkan}@ceng.metu.edu.tr
Department of Computer Engineering,
Middle East Technical University,
Ankara, Turkey, TR-06531
KEY WORDS: acquisition, calibration, digitization, modeling, registration, triangulation, high resolution, three-
dimensional, distortion, image, Internet, object, sequence, texture, web-based, photo-realism, virtual reality.
An image based model reconstruction system is described. Real images of a rigid object acquired under a simple but
controlled environment are used to recover the three dimensional geometry and the surface appearance. Proposed system
enables robustly modeling of small artifacts with high resolution geometry and surface appearance. Such artifacts may
have handles, holes, concavities, cracks, etc. The proposed system enables robustly modeling of these properties also. The
models obtained by this method are stored in Virtual Reality Modeling Language format which enables the transmission
and publishing of the models easier over Internet.
Realistic looking three dimensional (3D) models are im
portant components of virtual reality environments. They
are used in many multimedia applications such as 3D vir
tual simulators, 3D educational software, 3D object databases
and encyclopedias, 3D shopping over Internet and 3D tele
conferencing. The virtual environments enable the user in
teract with the models. The user can rotate, scale, even
deform the models; observe the models under different
lighting conditions; change the appearance (color, mate
rial, etc.) of the models; observe the interaction of a model
with the other models in the environment.
The most common way of creating 3D models for virtual
reality environments is manual design. This approach is
very suitable for the creation of the models of non-existing
objects. However, it is cost expensive and time consum
ing. Furthermore, the accuracy of the designed model for
a real object may not be satisfying. In fact, the number
of vertices of a simple model varies between 10000 and
100000, which is relatively high for manual design. In a
second approach, geometry of real objects is acquired us
ing a system that captures directly 3D data. Such systems
are constructed using expensive equipments such as laser
range scanners, structured light; touch based 3D scanners,
or 3D digitizers. In most of these active scanning systems,
the texture of the model is not captured while the geome
try of the object is acquired precisely as a set of points in
the 3D space. This set can then be converted to polygonal
model representations for rendering (Soucy et al., 1996).
In a third approach, the model of a real object is recon
structed from its two dimensional (2D) images. This tech
nique is known as image-based modeling. Even using an
off-the-shelf camera, considerably realistic looking models
with both geometry and texture is reconstructed (Niem and
Wingbermuhle, 1997, Matsumoto et al., 1997, Schmitt and
Yemez, 1999, Pollefeys et al., 2000, Mulayim and Atalay,
2001, Yilmaz et al., 2003, Mulayim et al., 2003).
Recent advances in computer vision in addition to pho-
togrammetry make it possible to acquire high resolution
3D models of scenes and objects. One of the most pop
ular applications has emerged as digitizing historical and
cultural heritance such as Digital Michelangelo and ACO-
HIR (Levoy et al., 2000, ESPRIT, 1998). However, recon
struction of a complex rigid object from its 2D images is
still a challenging computer vision problem under general
imaging conditions. Without a priori information about
the imaging*environment (camera geometry, lighting con
ditions, object and background surface properties, etc.), it
becomes very difficult to infer the 3D structure of the cap
tured object. For practical purposes, the problem can be
simplified by using controlled imaging environments. In
such an environment, camera makes a controlled motion
around the object, and background surface and lighting are
selected to reduce the specularities on the acquired im
ages (Niem and Wingbermiihle, 1997, Matsumoto et al.,
1997, Schmitt and Yemez, 1999, Miilayim et al., 1999,
Lensch et al., 2000, Ramanathan et al., 2000, Yilmaz et
al., 2003, Miilayim et al., 2003).
Figure 1: Image acquisition system consists of a turn table,
a camera and a computer.