rsion
+1) 1S
ionT
n Eq.
inear
tR=
ation
iz
(9)
ist six
linear
he six
tions
eriva-
points
er. To
set of
ion is
ation
ant to
y and
erva-
for R
tion is
pint is
n. An
estimate of the parameters R and T is calculated and
the observation point is moved according to those
parameters. The estimation is repeated with the new
starting position of P until the parameter changes of
T and R converge to zero.
To improve estimation stability, dependencies be-
tween rotation and translation parameters were can-
celled out through the introduction of a center of rota-
tion G. The rotation of an object around an arbitrary
rotation center can be separated into a rotation of the
object around the object's center of gravity and an
additional translation of the object. Such a decompo-
sition leads to an independent estimationof Rand T
and improves convergence of the solution.
The system should be robust against noisy measure-
ments or measurements which are erroneous due to
invalid model assumptions. Therefore the mean tem-
poral intensity is computed and observation points
with high intensity errors are excluded from the re-
gression in a modified least squares fit. The measure-
ment certainty of each parameter can be estimated
through evaluation of the error covariance matrix of
the regression [Hótter, 1988]. When a parameter has
an uncertain estimate it can be excluded from the
regressionto ensure a stable estimate forthe remain-
ing parameters. The analysis was calculated from a
monocular image sequence only. It has been tested
successfully on a variety of tasks for object and cam-
era motion tracking [Kappei, 1988],[Liedtke, 1990],
[Welz, 1990]. When including the stereoscopic se-
quence information the quality of the analysis is ex-
pected to improve further.
| en orinuible dept 3L
scene model
For each image pair of the sequence a depth map Dk
can be calculated by stereoscopic analysis together
with its associated confidence map Cy. The 3D scene
model contains the approximated scene geometry
that can be moved according to the camera and scene
motion. It is now possible to fuse the depth measure-
ments from multiple view points into the 3D scene
model to improve estimation quality. The confidence
value C is converted into the weight S that can easily
be accumulated throughout the sequence. Each con-
trol point of the scene objects holds not only its posi-
tion Pojg in space but also its corresponding confi-
dence weight Sg. When a new measurement
becomes available, the scene motion is compensated
and the new depth estimate Pew With corresponding
confidence weight Snew is integrated by weighted ac-
cumulation. Sçuse lepresents the accumulated quality
measure and Pruse the new control point position.
C
with S=—— (10)
Stuse = Sold + Snew 1—C
Pad 2 Sola T P. : Sicw
and Pose = S + S
old new
The information fusing process described above can
only be applied to an existing surface. When new
objects and prior unseen object surfaces appear, the
433
surface mesh must be extended from the new depth
map. Once the surface is built, the fusing process can
continue.
First results of the sequence analysis are shown in
Fig. 3d with the sequence "house". The house was
rotated on a turn table and 90 stereoscopic views of
the house from all directions, each view displaced by
4 degree rotation, were taken. Starting with the 3D
object shown in Fig. 3c, the 3D motion and rotation of
the house was estimated successfully. At present the
sequence analysis was tested with objects generated
from a single depth map only. The object part visible
from from one camera position was generated and
this object part was tracked throughoutthe sequence,
integrating the depth measurements from the differ-
ent view points. The resulting object surface after
integration from 6 different view points (0, 4, 8, 1 2,16,
and 20 degree rotation) is shown in Fig. 3d. The object
is rotated to a side view to show the still existing shape
deviations.
We are currently working to improve the motion esti-
mation by fully exploiting the stereoscopic sequence
information and to enhance the integration process.
It is necessary that the 3D object surfaces are gener-
ated not only from a single depth map but incremen-
tally when new surfaces appear. Additional quality
measures can be thought of that govern the global
surface shape and allow to introduce scene specific
knowledge.
ACKNOWLEDGEMENT
This work has been supported by a grant of the Ger-
man postal service TELEKOM.
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