PrimiTect: Fast Continuous Hough Voting for Primitive Detection
| Autor: | Sommer, Christiane, Sun, Yumin, Bylow, Erik, Cremers, Daniel |
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| Rok vydání: | 2020 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1109/ICRA40945.2020.9196988 |
| Popis: | 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. Comment: Accepted to IEEE International Conference on Robotics and Automation (ICRA), 2020 | Code: https://github.com/c-sommer/primitect |
| Databáze: | arXiv |
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