PrimiTect: Fast Continuous Hough Voting for Primitive Detection
It addresses data abstraction for robotics applications on low-power devices, but appears incremental as it builds on Hough voting schemes.
The paper tackles 3D point set data abstraction by classifying points into geometric primitives like planes and cones, resulting in a compact representation, and shows that the method outperforms state-of-the-art in accuracy and robustness.
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.