Fetching the paper…
Reading the bibliography…
Constructing a 3D scene capable of accommodating open-ended language queries, is a pivotal pursuit, particularly within the domain of robotics.
A. Neubeck and L. Van Gool, “Efficient non-maximum suppression,” in ICPR , 2006
2006
Earlier work this paper cites.
J. Redmon and A. Angelova, “Real-time grasp detection using convolutional neural networks,” in ICRA , 2015
2015
Earlier work this paper cites.
J. L. Schonberger and J.-M. Frahm, “Structure-from-motion revisited,” in CVPR , 2016
2016
Earlier work this paper cites.
J. Mahler, J. Liang, S. Niyaz, M. Laskey, R. Doan, X. Liu, J. A. Ojea, and K. Goldberg, “Dex-net 2.0: Deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics,” RSS , 2017
2017
Earlier work this paper cites.
D. Guo, F. Sun, H. Liu, T. Kong, B. Fang, and N. Xi, “A hybrid deep architecture for robotic grasp detection,” in ICRA , 2017
2017
Earlier work this paper cites.
X. Zhou, X. Lan, H. Zhang, Z. Tian, Y. Zhang, and N. Zheng, “Fully convolutional grasp detection network with oriented anchor box,” in IROS , 2018
2018
Earlier work this paper cites.
A. Ten Pas and R. Platt, “Using geometry to detect grasp poses in 3d point clouds,” Robotics Research: Volume 1 , 2018
2018
Earlier work this paper cites.
H. Liang, X. Ma, S. Li, M. Görner, S. Tang, B. Fang, F. Sun, and J. Zhang, “Pointnetgpd: Detecting grasp configurations from point sets,” in ICRA , 2019
2019
Earlier work this paper cites.
H.-Y. F. Tung, R. Cheng, and K. Fragkiadaki, “Learning spatial common sense with geometry-aware recurrent networks,” in CVPR , 2019
2019
Earlier work this paper cites.
C. Lynch and P. Sermanet, “Language conditioned imitation learning over unstructured data,” RSS , 2020
2020
Earlier work this paper cites.
D. Z. Chen, A. X. Chang, and M. Nießner, “Scanrefer: 3d object localization in rgb-d scans using natural language,” in ECCV , 2020
2020
Earlier work this paper cites.
C. Wu, J. Chen, Q. Cao, J. Zhang, Y. Tai, L. Sun, and K. Jia, “Grasp proposal networks: An end-to-end solution for visual learning of robotic grasps,” NeurIPS , 2020
2020
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 CVPR , 2020
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, et al. , “Learning transferable visual models from natural language supervision,” in ICML , 2021
2021
Earlier work this paper cites.
M. Caron, H. Touvron, I. Misra, H. Jégou, J. Mairal, P. Bojanowski, and A. Joulin, “Emerging properties in self-supervised vision transformers,” in ICCV , 2021
2021
Cited alongside, same era.
Z. Jiang, Y. Zhu, M. Svetlik, K. Fang, and Y. Zhu, “Synergies between affordance and geometry: 6-dof grasp detection via implicit representations,” RSS , 2021
2021
Cited alongside, same era.
X. Zhu, L. Sun, Y. Fan, and M. Tomizuka, “6-dof contrastive grasp proposal network,” in ICRA , 2021
2021
Cited alongside, same era.
M. Sundermeyer, A. Mousavian, R. Triebel, and D. Fox, “Contact-graspnet: Efficient 6-dof grasp generation in cluttered scenes,” in ICRA , 2021
2021
Cited alongside, same era.
B. Zhao, H. Zhang, X. Lan, H. Wang, Z. Tian, and N. Zheng, “Regnet: Region-based grasp network for end-to-end grasp detection in point clouds,” in ICRA , 2021
2021
A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A. C. Berg, W.-Y. Lo, et al. , “Segment anything,” in ICCV , 2023
2023
Later among the works it cites.
S. Chen, R. G. Pinel, C. Schmid, and I. Laptev, “Polarnet: 3d point clouds for language-guided robotic manipulation,” ArXiv , 2023
2023
Later among the works it cites.
Y. Ze, G. Yan, Y.-H. Wu, A. Macaluso, Y. Ge, J. Ye, N. Hansen, L. E. Li, and X. Wang, “Gnfactor: Multi-task real robot learning with generalizable neural feature fields,” in CoRL , 2023
2023
Later among the works it cites.
C. Zhong, Y. Zheng, Y. Zheng, H. Zhao, L. Yi, X. Mu, L. Wang, P. Li, G. Zhou, C. Yang, X. Zhang, and J. Zhao, “3d implicit transporter for temporally consistent keypoint discovery,” in ICCV , 2023
2023
Later among the works it cites.
J. Kerr, C. M. Kim, K. Goldberg, A. Kanazawa, and M. Tancik, “Lerf: Language embedded radiance fields,” in ICCV , 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
M. Shridhar, L. Manuelli, and D. Fox, “Cliport: What and where pathways for robotic manipulation,” in CoRL , 2022
2022
Cited alongside, same era.
P.-L. Guhur, S. Chen, R. G. Pinel, M. Tapaswi, I. Laptev, and C. Schmid, “Instruction-driven history-aware policies for robotic manipulations,” in CoRL , 2022
2022
Cited alongside, same era.
M. Shridhar, L. Manuelli, and D. Fox, “Perceiver-actor: A multi-task transformer for robotic manipulation,” in CoRL , 2022
2022
Cited alongside, same era.
S. Kobayashi, E. Matsumoto, and V. Sitzmann, “Decomposing nerf for editing via feature field distillation,” NeurIPS , 2022
2022
Cited alongside, same era.
V. Tschernezki, I. Laina, D. Larlus, and A. Vedaldi, “Neural feature fusion fields: 3d distillation of self-supervised 2d image representations,” in 3DV , 2022
2022
Cited alongside, same era.
A. Alliegro, M. Rudorfer, F. Frattin, A. Leonardis, and T. Tommasi, “End-to-end learning to grasp via sampling from object point clouds,” RA-L , 2022
2022
Cited alongside, same era.
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,” RA-L , 2022
2022
Cited alongside, same era.
2023
Later among the works it cites.
W. Shen, G. Yang, A. Yu, J. Wong, L. P. Kaelbling, and P. Isola, “Distilled feature fields enable few-shot language-guided manipulation,” in CoRL , 2023
2023
Later among the works it cites.
A. Rashid, S. Sharma, C. M. Kim, J. Kerr, L. Y. Chen, A. Kanazawa, and K. Goldberg, “Language embedded radiance fields for zero-shot task-oriented grasping,” in CoRL , 2023
2023
Later among the works it cites.
B. Kerbl, G. Kopanas, T. Leimkühler, and G. Drettakis, “3d gaussian splatting for real-time radiance field rendering,” ACM Transactions on Graphics , 2023
2023
Later among the works it cites.
Y. Zheng, Q. Wang, C. Zhong, H. Liang, Z. Han, and Y. Zheng, “Enhancing daily life through an interactive desktop robotics system,” in CICAI , 2023
2023
Later among the works it cites.
G. Zhai, D. Huang, S.-C. Wu, H. Jung, Y. Di, F. Manhardt, F. Tombari, N. Navab, and B. Busam, “Monograspnet: 6-dof grasping with a single rgb image,” in ICRA , 2023
2023
Later among the works it cites.
H.-S. Fang, C. Wang, H. Fang, M. Gou, J. Liu, H. Yan, W. Liu, Y. Xie, and C. Lu, “Anygrasp: Robust and efficient grasp perception in spatial and temporal domains,” T-RO , 2023
2023
Later among the works it cites.
S. Peng, K. Genova, C. Jiang, A. Tagliasacchi, M. Pollefeys, T. Funkhouser, et al. , “Openscene: 3d scene understanding with open vocabularies,” in CVPR , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.