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In this paper, we tackle the problem of grasping transparent and specular objects.
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M. Sundermeyer, A. Mousavian, R. Triebel, and D. Fox, “Contact-graspnet: Efficient 6-dof grasp generation in cluttered scenes,” in 2021 IEEE International Conference on Robotics and Automation (ICRA)
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M. Gou, H.-S. Fang, Z. Zhu, S. Xu, C. Wang, and C. Lu, “Rgb matters: Learning 7-dof grasp poses on monocular rgbd images,” in 2021 IEEE International Conference on Robotics and Automation (ICRA)
2021
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C. Wang, H.-S. Fang, M. Gou, H. Fang, J. Gao, and C. Lu, “Graspness discovery in clutters for fast and accurate grasp detection,” in Proceedings of the IEEE/CVF International Conference on Computer Vision
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L. Lipson, Z. Teed, and J. Deng, “Raft-stereo: Multilevel recurrent field transforms for stereo matching,” in 2021 International Conference on 3D Vision (3DV)
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Y. Tang, J. Chen, Z. Yang, Z. Lin, Q. Li, and W. Liu, “Depthgrasp: depth completion of transparent objects using self-attentive adversarial network with spectral residual for grasping,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
2021
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2023
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