PrimiTect: Fast Continuous Hough Voting for Primitive Detection
Sommer C, Sun Y, Bylow E, Cremers D (2020)
Publication Type: Conference contribution
Publication year: 2020
Journal
Publisher: Institute of Electrical and Electronics Engineers Inc.
Pages Range: 8404-8410
Conference Proceedings Title: Proceedings - IEEE International Conference on Robotics and Automation
Event location: Paris, FRA
ISBN: 9781728173955
DOI: 10.1109/ICRA40945.2020.9196988
Abstract
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.
Involved external institutions
Germany (DE)How to cite
APA:
Sommer, C., Sun, Y., Bylow, E., & Cremers, D. (2020). PrimiTect: Fast Continuous Hough Voting for Primitive Detection. In Proceedings - IEEE International Conference on Robotics and Automation (pp. 8404-8410). Paris, FRA: Institute of Electrical and Electronics Engineers Inc..
MLA:
Sommer, Christiane, et al. "PrimiTect: Fast Continuous Hough Voting for Primitive Detection." Proceedings of the 2020 IEEE International Conference on Robotics and Automation, ICRA 2020, Paris, FRA Institute of Electrical and Electronics Engineers Inc., 2020. 8404-8410.
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