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Category-level object pose estimation, which predicts the pose of objects within a known category without prior knowledge of individual instances, is essential in applications like warehouse automation and manufacturing.
B. Drost, M. Ulrich, N. Navab, and S. Ilic, “Model globally, match locally: Efficient and robust 3d object recognition,” in 2010 IEEE computer society conference on computer vision and pattern recognition . Ieee, 2010, pp. 998–1005
2010
Earlier work this paper cites.
S. Hinterstoisser, V. Lepetit, S. Ilic, S. Holzer, G. Bradski, K. Konolige, and N. Navab, “Model based training, detection and pose estimation of texture-less 3d objects in heavily cluttered scenes,” in Computer Vision–ACCV 2012: 11th Asian Conference on Computer Vision, Daejeon, Korea, November 5-9, 2012, Revised Selected Papers, Part I 11 . Springer, 2013, pp. 548–562
2013
Earlier work this paper cites.
2017
Earlier work this paper cites.
C. R. Qi, L. Yi, H. Su, and L. J. Guibas, “Pointnet++: Deep hierarchical feature learning on point sets in a metric space,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask r-cnn,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 2961–2969
2017
Earlier work this paper cites.
C. Wang, D. Xu, Y. Zhu, R. Martín-Martín, C. Lu, L. Fei-Fei, and S. Savarese, “Densefusion: 6d object pose estimation by iterative dense fusion,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 3343–3352
2019
Earlier work this paper cites.
S. Peng, Y. Liu, Q. Huang, X. Zhou, and H. Bao, “Pvnet: Pixel-wise voting network for 6dof pose estimation,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 4561–4570
2019
Earlier work this paper cites.
H. Wang, S. Sridhar, J. Huang, J. Valentin, S. Song, and L. J. Guibas, “Normalized object coordinate space for category-level 6d object pose and size estimation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 2642–2651
2019
Earlier work this paper cites.
G. Wang, F. Manhardt, J. Shao, X. Ji, N. Navab, and F. Tombari, “Self6d: Self-supervised monocular 6d object pose estimation,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part I 16 . Springer, 2020, pp. 108–125
2020
Earlier work this paper cites.
M. Tian, M. H. Ang, and G. H. Lee, “Shape prior deformation for categorical 6d object pose and size estimation,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXI 16 . Springer, 2020, pp. 530–546
2020
Earlier work this paper cites.
W. Chen, X. Jia, H. J. Chang, J. Duan, L. Shen, and A. Leonardis, “Fs-net: Fast shape-based network for category-level 6d object pose estimation with decoupled rotation mechanism,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 1581–1590
2021
Earlier work this paper cites.
K. Chen and Q. Dou, “Sgpa: Structure-guided prior adaptation for category-level 6d object pose estimation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 2773–2782
2021
Earlier work this paper cites.
J. Lin, Z. Wei, Z. Li, S. Xu, K. Jia, and Y. Li, “Dualposenet: Category-level 6d object pose and size estimation using dual pose network with refined learning of pose consistency,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 3560–3569
2021
Cited alongside, same era.
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 International conference on machine learning . PMLR, 2021, pp. 8748–8763
2021
Cited alongside, same era.
D. Cai, J. Heikkilä, and E. Rahtu, “Ove6d: Object viewpoint encoding for depth-based 6d object pose estimation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 6803–6813
2022
Cited alongside, same era.
H. Lin, Z. Liu, C. Cheang, Y. Fu, G. Guo, and X. Xue, “Sar-net: Shape alignment and recovery network for category-level 6d object pose and size estimation,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 6707–6717
2023
Later among the works it cites.
X. Lin, W. Yang, Y. Gao, and T. Zhang, “Instance-adaptive and geometric-aware keypoint learning for category-level 6d object pose estimation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 21 040–21 049
2024
Later among the works it cites.
R. Zhang, Z. Huang, G. Wang, C. Zhang, Y. Di, X. Zuo, J. Tang, and X. Ji, “Lapose: Laplacian mixture shape modeling for rgb-based category-level object pose estimation,” in European Conference on Computer Vision . Springer, 2024, pp. 467–484
2024
Later among the works it cites.
J. Zhang, M. Wu, and H. Dong, “Generative category-level object pose estimation via diffusion models,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
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2022
Cited alongside, same era.
R. Zhang, Y. Di, Z. Lou, F. Manhardt, F. Tombari, and X. Ji, “Rbp-pose: Residual bounding box projection for category-level pose estimation,” in European Conference on Computer Vision . Springer, 2022, pp. 655–672
2022
Cited alongside, same era.
Y. Di, R. Zhang, Z. Lou, F. Manhardt, X. Ji, N. Navab, and F. Tombari, “Gpv-pose: Category-level object pose estimation via geometry-guided point-wise voting,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 6781–6791
2022
Cited alongside, same era.
J. Lin, Z. Wei, C. Ding, and K. Jia, “Category-level 6d object pose and size estimation using self-supervised deep prior deformation networks,” in European Conference on Computer Vision . Springer, 2022, pp. 19–34
2022
Cited alongside, same era.
Y. You, R. Shi, W. Wang, and C. Lu, “Cppf: Towards robust category-level 9d pose estimation in the wild,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 6866–6875
2022
Cited alongside, same era.
H. Zhang, J. Peeters, E. Demeester, and K. Kellens, “Deep learning reactive robotic grasping with a versatile vacuum gripper,” IEEE Transactions on Robotics , vol. 39, no. 2, pp. 1244–1259, 2022
2022
Cited alongside, same era.
F. Duffhauss, S. Koch, H. Ziesche, N. A. Vien, and G. Neumann, “Symfm6d: Symmetry-aware multi-directional fusion for multi-view 6d object pose estimation,” IEEE Robotics and Automation Letters , 2023
2023
Cited alongside, same era.
J. Liu, Y. Chen, X. Ye, and X. Qi, “Prior-free category-level pose estimation with implicit space transformation,” in IEEE International Conference on Computer Vision 2023 (02/10/2023-06/10/2023, Paris) , 2023
2023
Cited alongside, same era.
R. Wang, X. Wang, T. Li, R. Yang, M. Wan, and W. Liu, “Query6dof: Learning sparse queries as implicit shape prior for category-level 6dof pose estimation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 14 055–14 064
2023
Cited alongside, same era.
Later among the works it cites.
J. Zhang, W. Huang, B. Peng, M. Wu, F. Hu, Z. Chen, B. Zhao, and H. Dong, “Omni6dpose: a benchmark and model for universal 6d object pose estimation and tracking,” in European Conference on Computer Vision . Springer, 2024, pp. 199–216
2024
Later among the works it cites.
J. Cai, Y. He, W. Yuan, S. Zhu, Z. Dong, L. Bo, and Q. Chen, “Open-vocabulary category-level object pose and size estimation,” IEEE Robotics and Automation Letters , 2024
2024
Later among the works it cites.
P. Wang, T. Ikeda, R. Lee, and K. Nishiwaki, “Gs-pose: Category-level object pose estimation via geometric and semantic correspondence,” in European Conference on Computer Vision . Springer, 2024, pp. 108–126
2024
Later among the works it cites.
T. Ikeda, S. Zakharov, T. Ko, M. Z. Irshad, R. Lee, K. Liu, R. Ambrus, and K. Nishiwaki, “Diffusionnocs: Managing symmetry and uncertainty in sim2real multi-modal category-level pose estimation,” in 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2024, pp. 7406–7413
2024
Later among the works it cites.
Y. Yang, H. Yu, X. Lou, Y. Liu, and C. Choi, “Attribute-based robotic grasping with data-efficient adaptation,” IEEE Transactions on Robotics , 2024
2024
Later among the works it cites.
W. Wei, P. Wang, S. Wang, Y. Luo, W. Li, D. Li, Y. Huang, and H. Duan, “Learning human-like functional grasping for multi-finger hands from few demonstrations,” IEEE Transactions on Robotics , 2024
2024
Later among the works it cites.
Y. Yang, Z. Cui, Q. Zhang, and J. Liu, “Ps6d: Point cloud based symmetry-aware 6d object pose estimation in robot bin-picking,” in 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2024, pp. 7167–7174
2024
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2024
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