GDN: A Coarse-To-Fine (C2F) Representation for End-To-End 6-DoF Grasp Detection

• URL: http://arxiv.org/abs/2010.10695v4
• Date: Wed, 11 Nov 2020 07:00:03 GMT
• Title: GDN: A Coarse-To-Fine (C2F) Representation for End-To-End 6-DoF Grasp Detection
• Authors: Kuang-Yu Jeng, Yueh-Cheng Liu, Zhe Yu Liu, Jen-Wei Wang, Ya-Liang Chang, Hung-Ting Su, and Winston H. Hsu
• Abstract summary: We propose an end-to-end grasp detection network, Grasp Detection Network (GDN) Compared to previous two-stage approaches, our architecture is at least 20 times faster. We propose a new AP-based metric which considers both rotation and transition errors.
• Score: 23.480036081293242
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We proposed an end-to-end grasp detection network, Grasp Detection Network (GDN), cooperated with a novel coarse-to-fine (C2F) grasp representation design to detect diverse and accurate 6-DoF grasps based on point clouds. Compared to previous two-stage approaches which sample and evaluate multiple grasp candidates, our architecture is at least 20 times faster. It is also 8% and 40% more accurate in terms of the success rate in single object scenes and the complete rate in clutter scenes, respectively. Our method shows superior results among settings with different number of views and input points. Moreover, we propose a new AP-based metric which considers both rotation and transition errors, making it a more comprehensive evaluation tool for grasp detection models.

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