CIPA 2003 XIX th International Symposium, 30 September - 04 October, 2003, Antalya, Turkey
angles and distances. Besides, these representations of historical
artifacts are enriched by online explanations, animations, music,
video and narrations. This allows virtual museum visitors use
this computer generated environment interactively for cultural
research or educational purposes. In virtual museums there
might be large numbers of dynamic virtual objects; therefore
virtual objects should be modeled effectively. In order to
increase realism of the virtual world, objects can be modeled
from their images.
In this paper we use voxel-based methods to recover the shape
of the cultural heritage artifacts. Voxel methods consume large
amounts of memory, for example 512 3 bytes (128 mbytes) for a
medium size cube (512 units in each direction). Since there are
rapid advances in hardware however, this problem is becoming
less important and volumetric representations is becoming more
attractive.
The model of the 3D object can be easily acquired by shape
from silhouettes methods in which the shape of the objects is
recovered by intersecting the volumes. The intersection of
silhouette cones from multiple images gives a good estimation
of the true model. This approximate model is called the visual
hull (Laurentini, 1995), (Matusik et al, 2000). Shape from
silhouette methods is fast and robust; however the concavities
and the critical areas on an object cannot be recovered with this
method because the viewing region doesn’t completely
surround the object. The later work to recover the shape of the
objects from multiple images is concentrated on voxel coloring
algorithms (Seitz and Dyer, 1997), (Culbertson et al, 1999),
(Kuzu and Sinram, 2002). These algorithms use color
consistency to distinguish surface points from the other points
in the scene. They use the fact that surface points in a scene
project into consistent (similar) colors in the input images.
In this paper we describe several tools to refine the object’s
visual hull.
The organization of the chapters is as follows: In chapter 2 the
image acquisition setup is described. The image orientation
process is also explained. The reconstruction of the model using
shape from silhouette technique will be described in chapter 3,
where the refinement tools are introduced as well. Also there,
an effective method to recover visibility information will be
introduced. Furthermore, this information will be enhanced with
a quality measure, by using the surface normal vector of a voxel
in combination with the viewing direction of the image. In
chapter 4 the refinement algorithm is explained.
2. IMAGE ACQUISITION SETUP
The system requirements of our experiment is simple, we use a
CCD video-camera in order to acquire still images of the
artifacts. Moreover, we use a calibration object to compute the
interior orientation parameters of the camera. We place the
object in front of a blue background. Image segmentation is a
requirement to recover visual hull of the object. In order to
segment object pixels from background pixels we place the
object in front of a homogeneous blue background. We capture
multiple views of the object resulting in a circular camera setup.
2.1 Camera Calibration and Determination of Control
Points
Before acquiring the images, the camera should be calibrated.
In our experiment we use a standard CCD video camera with
auto focus. The focus of the camera can be fixed but we cannot
tell whether it is unchanged since the last use. Hence, we
calibrated the sensor using several images with a special
calibration object which provides a good coverage of the
objects having three perpendicular square planes and 25 control
points on each side.
In a second session, the object is placed inside the calibration
frame in order to define some natural control points accurately,
as shown in figure 1.
A bundle block adjustment including all the images delivered
not only the interior camera parameters, but also the coordinates
of new points on the vase, which will serve as control points in
the space carving processes.
During the subsequent image acquisitions, the focus remained
fixed.
Figure 1: Camera calibration and control point determination
2.2 Image Orientation
In order to model the objects accurately, the images should be
oriented. As mentioned above, we determined some natural
control points on the objects surface, since we should not put
markings on an historical artifact. They were defined in an
arbitrarily chosen coordinate system, since there is no need to
have the coordinates in a specific higher-level coordinate
system.
The images were adjusted in a bundle block adjustment process.
We used enough tie points in all images in the circular camera
setup to perform a bundle block adjustment, covering all
images. Figure 2 shows an OpenGL visualization of the
situation. We achieved very accurate results for the image
orientations, using the previously calibrated camera.
3. VOXEL-BASED ALGORITHMS
3.1 Shape from Silhouette
Shape from silhouette is a well-known approach for recovering
the shape of the objects from their contours. This approach is
popular in computer vision and in computer graphics due to its
fast computation and robustness.
As a precondition of volume intersection algorithms, the
contour of the real object must be extracted from input images.
In this experiment a monochromatic blue background was used
