3-D Cueing: A Data Filter for Object Recognition

This paper presents a novel method for quickly filtering range data points to make object recognition in large 3D data sets feasible. The general approach, called " 3D cueing, " uses shape signatures from object models as the basis for a fast, probabilistic classification system which rates scene points in terms of their likelihood of belonging to a model. This algorithm, which could be used as a front-end for any traditional 3D matching technique, is demonstrated using several models and cluttered scenes in which the model occupies between 1% and 50% of the data points.