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In this work, we tackle 6-DoF grasp detection for transparent and specular objects, which is an important yet challenging problem in vision-based robotic systems, due to the failure of depth cameras in sensing their geometry.
B. O. Community, Blender - a 3D modelling and rendering package , Blender Foundation, Stichting Blender Foundation, Amsterdam, 2018. [Online]. Available: http://www.blender.org
2018
Earlier work this paper cites.
Z. Zhou, T. Pan, S. Wu, H. Chang, and O. C. Jenkins, “Glassloc: plenoptic grasp pose detection in transparent clutter,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2019, pp. 4776–4783
2019
Earlier work this paper cites.
H.-S. Fang, C. Wang, M. Gou, and C. Lu, “Graspnet-1billion: A large-scale benchmark for general object grasping,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 11 444–11 453
2020
Earlier work this paper cites.
S. Sajjan, M. Moore, M. Pan, G. Nagaraja, J. Lee, A. Zeng, and S. Song, “Clear grasp: 3d shape estimation of transparent objects for manipulation,” in 2020 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2020, pp. 3634–3642
2020
Earlier work this paper cites.
B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, and R. Ng, “Nerf: Representing scenes as neural radiance fields for view synthesis,” in ECCV , 2020
2020
Earlier work this paper cites.
T. Weng, A. Pallankize, Y. Tang, O. Kroemer, and D. Held, “Multi-modal transfer learning for grasping transparent and specular objects,” IEEE Robotics and Automation Letters , vol. 5, no. 3, pp. 3791–3798, 2020
2020
Earlier work this paper cites.
A. Gropp, L. Yariv, N. Haim, M. Atzmon, and Y. Lipman, “Implicit geometric regularization for learning shapes,” in International Conference on Machine Learning . PMLR, 2020, pp. 3789–3799
2020
Earlier work this paper cites.
M. Breyer, J. J. Chung, L. Ott, R. Siegwart, and J. Nieto, “Volumetric grasping network: Real-time 6 dof grasp detection in clutter,” in Conference on Robot Learning . PMLR, 2021, pp. 1602–1611
2021
Earlier work this paper cites.
Z. Jiang, Y. Zhu, M. Svetlik, K. Fang, and Y. Zhu, “Synergies between affordance and geometry: 6-dof grasp detection via implicit representations,” Robotics: science and systems , 2021
2021
Earlier work this paper cites.
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) , 2021
2021
Cited alongside, same era.
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 Proceedings of the International Conference on Robotics and Automation (ICRA) , 2021
2021
Cited alongside, same era.
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 (ICCV) , October 2021, pp. 15 964–15 973
2021
Cited alongside, same era.
A. Chen, Z. Xu, F. Zhao, X. Zhang, F. Xiang, J. Yu, and H. Su, “Mvsnerf: Fast generalizable radiance field reconstruction from multi-view stereo,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 14 124–14 133
H. Fang, H.-S. Fang, S. Xu, and C. Lu, “Transcg: A large-scale real-world dataset for transparent object depth completion and a grasping baseline,” IEEE Robotics and Automation Letters , pp. 1–8, 2022
2022
Closest in time.
Q. Dai, J. Zhang, Q. Li, T. Wu, H. Dong, Z. Liu, P. Tan, and H. Wang, “Domain randomization-enhanced depth simulation and restoration for perceiving and grasping specular and transparent objects,” in European Conference on Computer Vision (ECCV) , 2022
2022
Closest in time.
J. Ichnowski, Y. Avigal, J. Kerr, and K. Goldberg, “Dex-nerf: Using a neural radiance field to grasp transparent objects,” in Conference on Robot Learning . PMLR, 2022, pp. 526–536
2022
Closest in time.
J. Kerr, L. Fu, H. Huang, J. Ichnowski, M. Tancik, Y. Avigal, A. Kanazawa, and K. Goldberg, “Evo-neRF: Evolving neRF for sequential robot grasping,” in 6th Annual Conference on Robot Learning , 2022. [Online]. Available: https://openreview.net/forum?id=Bxr45keYrf
2022
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2021
Cited alongside, same era.
H. Cao, J. Huang, Y. Li, J. Zhou, and Y. Liu, “Fuzzy-depth objects grasping based on fsg algorithm and a soft robotic hand,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2021, pp. 3948–3954
2021
Cited alongside, same era.
J. Chang, M. Kim, S. Kang, H. Han, S. Hong, K. Jang, and S. Kang, “Ghostpose*: Multi-view pose estimation of transparent objects for robot hand grasping,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2021, pp. 5749–5755
2021
Cited alongside, same era.
P. Wang, L. Liu, Y. Liu, C. Theobalt, T. Komura, and W. Wang, “Neus: Learning neural implicit surfaces by volume rendering for multi-view reconstruction,” NeurIPS , 2021
2021
Cited alongside, same era.
E. Coumans and Y. Bai, “Pybullet, a python module for physics simulation for games, robotics and machine learning,” http://pybullet.org
2021
Cited alongside, same era.
Closest in time.
T. Müller, A. Evans, C. Schied, and A. Keller, “Instant neural graphics primitives with a multiresolution hash encoding,” ACM Trans. Graph. , vol. 41, no. 4, pp. 102:1–102:15, Jul. 2022. [Online]. Available: https://doi.org/10.1145/3528223.3530127
2022
Closest in time.
Y. Liu, S. Peng, L. Liu, Q. Wang, P. Wang, C. Theobalt, X. Zhou, and W. Wang, “Neural rays for occlusion-aware image-based rendering,” in CVPR , 2022
2022
Closest in time.
L. Yen-Chen, P. Florence, J. T. Barron, T.-Y. Lin, A. Rodriguez, and P. Isola, “NeRF-Supervision: Learning dense object descriptors from neural radiance fields,” in IEEE Conference on Robotics and Automation (ICRA) , 2022
2022
Closest in time.
Y.-C. Lin, P. Florence, A. Zeng, J. T. Barron, Y. Du, W.-C. Ma, A. Simeonov, A. R. Garcia, and P. Isola, “MIRA: Mental imagery for robotic affordances,” in 6th Annual Conference on Robot Learning , 2022. [Online]. Available: https://openreview.net/forum?id=AmPeAFzU3a4
2022
Closest in time.