A task driven 3D object recognition system using Bayesian networks

In this paper we propose a general framework to build a task oriented 3D object recognition system for CAD based vision (CBV). Features from 3D space curves representing the object's rims provide sufficient information to allow identification and pose estimation of industrial CAD models. However, features relying on differential surface properties tend to be very vulnerable with respect to noise. To model the statistical behavior of the data we introduce Bayesian nets which model the relationship between objects and observable features. Furthermore, task oriented selection of the optimal action to reduce the uncertainty of recognition results is incorporated into the Bayesian nets. This enables the integration of intelligent recognition strategies depending on the already acquired evidence into a robust, and efficient, 3D CAD based recognition system.