  This paper tackles the problem of data abstraction in the context of 3D point
sets. Our method classifies points into different geometric primitives, such as
planes and cones, leading to a compact representation of the data. Being based
on a semi-global Hough voting scheme, the method does not need initialization
and is robust, accurate, and efficient. We use a local, low-dimensional
parameterization of primitives to determine type, shape and pose of the object
that a point belongs to. This makes our algorithm suitable to run on devices
with low computational power, as often required in robotics applications. The
evaluation shows that our method outperforms state-of-the-art methods both in
terms of accuracy and robustness.
