Simultaneous Multiple 3D Motion Estimation via Mode Finding on Lie Groups
Oncel Tuzel(1), Raghav Subbarao(2) and Peter Meer(1,2)
(1) Department of Computer Science
(2) Department of Electrical and Computer Engineering
Rutgers University, Piscataway, NJ 08854, USA
We propose a new method to estimate multiple rigid motions
from noisy 3D point correspondences in the presence
of outliers. The method does not require prior specification
of number of motion groups and estimates all the motion
parameters simultaneously. We start with generating samples
from the rigid motion distribution. The motion parameters
are then estimated via mode finding operations on the
sampled distribution. Since rigid motions do not lie on a
vector space, classical statistical methods can not be used
for mode finding. We develop a mean shift algorithm which
estimates modes of the sampled distribution using the Lie
group structure of the rigid motions. We also show that proposed
mean shift algorithm is general and can be applied
to any distribution having a matrix Lie group structure. Experimental
results on synthetic and real image data demonstrate
the superior performance of the algorithm.
10th IEEE International Conference on
Computer Vision.,
Beijing, China, October 2005, vol. I, 18-25.
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