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Abstract

We present an approach for estimating the 3D position and
in case of articulated objects also the joint configuration from segmented
2D images. The pose estimation without initial information is a challenging
optimization problem in a high dimensional space and is essential for
texture acquisition and initialization of model-based tracking algorithms.
Our method is able to recognize the correct object in the case of multiple
objects and estimates its pose with a high accuracy. The key component
is a particle-based global optimization method that converges to the
global minimum similar to simulated annealing. After detecting potential
bounded subsets of the search space, the particles are divided into
clusters and migrate to the most attractive cluster as the time increases.
The performance of our approach is verified by means of real scenes and a
quantative error analysis for image distortions. Our experiments include
rigid bodies and full human bodies.