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Recent 3D-based manipulation methods either directly predict the grasp pose using 3D neural networks, or solve the grasp pose using similar objects retrieved from shape databases.
1901
Earlier work this paper cites.
1903
Earlier work this paper cites.
1904
Earlier work this paper cites.
1904
Earlier work this paper cites.
1904
Earlier work this paper cites.
J. Mahler, F. T. Pokorny, B. Hou, M. Roderick, M. Laskey, M. Aubry, K. Kohlhoff, T. Kröger, J. Kuffner, and K. Goldberg, “Dex-net 1.0: A cloud-based network of 3d objects for robust grasp planning using a multi-armed bandit model with correlated rewards,” in 2016 IEEE International Conference on Robotics and Automation (ICRA) , 2016, pp. 1957–1964
1964
Earlier work this paper cites.
2001
Earlier work this paper cites.
2003
Earlier work this paper cites.
2003
Earlier work this paper cites.
2003
Earlier work this paper cites.
2008
Earlier work this paper cites.
2011
Earlier work this paper cites.
M. Nießner, M. Zollhöfer, S. Izadi, and M. Stamminger, “Real-time 3d reconstruction at scale using voxel hashing,” ACM Trans. Graph. , vol. 32, no. 6, pp. 169:1–169:11, 2013. [Online]. Available: https://doi.org/10.1145/2508363.2508374
2013
Earlier work this paper cites.
J. T. Barron and J. Malik, “Intrinsic scene properties from a single rgb-d image,” CVPR , 2013
2013
Earlier work this paper cites.
A. Handa, T. Whelan, J. McDonald, and A. J. Davison, “A benchmark for rgb-d visual odometry, 3d reconstruction and slam,” ICRA , 2014
2014
Earlier work this paper cites.
J. Bohg, J. Romero, A. Herzog, and S. Schaal, “Robot arm pose estimation through pixel-wise part classification,” ICRA , 2014
2014
Earlier work this paper cites.
A. Chang, T. Funkhouser, L. Guibas, P. Hanrahan, Q. Huang, Z. Li, S. Savarese, M. Savva, S. Song, H. Su, J. Xiao, L. Yi, and F. Yu, “Shapenet: An information-rich 3d model repository,” 12 2015
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
2016
Earlier work this paper cites.
2016
Cited alongside, same era.
K. Mamou, “Volumetric approximate convex decomposition,” in Game Engine Gems 3 , E. Lengyel, Ed. A K Peters / CRC Press, 2016, ch. 12, pp. 141–158
2016
Cited alongside, same era.
2017
Cited alongside, same era.
2017
Cited alongside, same era.
R. Chen, S. Han, J. Xu, and H. Su, “Visibility-aware point-based multi-view stereo network,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 43, no. 10, pp. 3695–3708, 2021. [Online]. Available: https://doi.org/10.1109/TPAMI.2020.2988729
2020
Later among the works it cites.
2021
Later among the works it cites.
H. Fu, B. Cai, L. Gao, L.-X. Zhang, J. Wang, C. Li, Q. Zeng, C. Sun, R. Jia, B. Zhao et al. , “3d-front: 3d furnished rooms with layouts and semantics,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 10 933–10 942
2021
Later among the works it cites.
H. Fu, R. Jia, L. Gao, M. Gong, B. Zhao, S. Maybank, and D. Tao, “3d-future: 3d furniture shape with texture,” International Journal of Computer Vision , pp. 1–25, 2021
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2017
Cited alongside, same era.
2017
Cited alongside, same era.
2017
Cited alongside, same era.
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2018
Cited alongside, same era.
N. Wang, Y. Zhang, Z. Li, Y. Fu, W. Liu, and Y. Jiang, “Pixel2mesh: Generating 3d mesh models from single RGB images,” in Computer Vision - ECCV 2018 - 15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part XI , ser. Lecture Notes in Computer Science, V. Ferrari, M. Hebert, C. Sminchisescu, and Y. Weiss, Eds., vol. 11215. Springer, 2018, pp. 55–71. [Online]. Available: https://doi.org/10.1007/978-3-030-01252-6_4
2018
Cited alongside, same era.
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
Z. Xie, X. Da, M. van de Panne, B. Babich, and A. Garg, “Dynamics randomization revisited: A case study for quadrupedal locomotion,” in IEEE International Conference on Robotics and Automation, ICRA 2021, Xi’an, China, May 30 - June 5, 2021 . IEEE, 2021, pp. 4955–4961. [Online]. Available: https://doi.org/10.1109/ICRA48506.2021.9560837
2021
Later among the works it cites.
L. Minghua and J. Gu, “Mplib,” https://github.com/haosulab/MPlib , 2021
2021
Later among the works it cites.
2022
Closest in time.
W. Zhou and D. Held, “Learning to grasp the ungraspable with emergent extrinsic dexterity,” in ICRA 2022 Workshop: Reinforcement Learning for Contact-Rich Manipulation , 2022. [Online]. Available: https://openreview.net/forum?id=Zrp4wpa9lqh
2022
Closest in time.
A. Agarwal, A. Kumar, J. Malik, and D. Pathak, “Legged locomotion in challenging terrains using egocentric vision,” in 6th Annual Conference on Robot Learning , 2022. [Online]. Available: https://openreview.net/forum?id=Re3NjSwf0WF
2022
Closest in time.
2022
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2022
Closest in time.
J. Lv, Q. Yu, L. Shao, W. Liu, W. Xu, and C. Lu, “SAGCI-system: Towards sample-efficient, generalizable, compositional, and incremental robot learning,” in ICRA 2022 Workshop: Reinforcement Learning for Contact-Rich Manipulation , 2022. [Online]. Available: https://openreview.net/forum?id=Fdj08qJuZxH
2022
Closest in time.