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.
@article{arxiv.2005.07457,
title = {PrimiTect: Fast Continuous Hough Voting for Primitive Detection},
author = {Christiane Sommer and Yumin Sun and Erik Bylow and Daniel Cremers},
journal= {arXiv preprint arXiv:2005.07457},
year = {2021}
}
Comments
Accepted to IEEE International Conference on Robotics and Automation (ICRA), 2020 | Code: https://github.com/c-sommer/primitect