Optimal Feature-set Selection Controlled by Pose-space Location

In this paper a novel feature subset selection method for model-based 3D-pose recovery is introduced. Many different kind of features were applied to correspondence-based pose recovery tasks. Every single feature has advantages and disadvantages based on the object’s properties like shape, texture or size. For that reason it is worthwhile to select features with special attention to object’s properties. This selection process was the topic of several publications in the past. Since the object’s are not static but rotatable and even flexible, their properties change depends on there pose configuration. In consequence the feature selection process has different results when pose configuration changes. That is the point where the proposed method comes into play: it selects and combines features regarding the objects pose-space location and creates several different feature subsets. An exemplary test run at the end of the paper shows that the method decreases the runtime and increases the accuracy of the matching process.