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Prior work on 6-DoF object pose estimation has largely focused on instance-level processing, in which a textured CAD model is available for each object being detected.
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2001
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T. Moons, L. Van Gool, and M. Vergauwen, 3D reconstruction from multiple images: Principles . Now Foundations and Trends, 2009
2009
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C. Choi and H. I. Christensen, “3D textureless object detection and tracking: An edge-based approach,” in IROS , 2012, pp. 3877–3884
2012
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M. Lourakis and X. Zabulis, “Accurate scale factor estimation in 3D reconstruction,” in International Conference on Computer Analysis of Images and Patterns , 2013, pp. 498–506
2013
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B. Calli, A. Walsman, A. Singh, S. Srinivasa, P. Abbeel, and A. M. Dollar, “Benchmarking in manipulation research: Using the Yale-CMU-Berkeley object and model set,” IEEE Robotics and Automation Magazine , vol. 22, no. 3, Sep. 2015
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2015
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T. Birdal and S. Ilic, “Point pair features based object detection and pose estimation revisited,” in 3DV , 2015, pp. 527–535
2015
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Y. I. Abdel-Aziz, H. M. Karara, and M. Hauck, “Direct linear transformation from comparator coordinates into object space coordinates in close-range photogrammetry,” Photogrammetric Engineering & Remote Sensing , vol. 81, no. 2, pp. 103–107, 2015
2015
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N. Ballas, L. Yao, C. Pal, and A. C. Courville, “Delving deeper into convolutional networks for learning video representations.” in ICLR , 2016
2016
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A. Zeng, K.-T. Yu, S. Song, D. Suo, E. Walker, A. Rodriguez, and J. Xiao, “Multi-view self-supervised deep learning for 6D pose estimation in the amazon picking challenge,” in ICRA , 2017, pp. 1386–1383
2017
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M. Rad and V. Lepetit, “BB8: A scalable, accurate, robust to partial occlusion method for predicting the 3D poses of challenging objects without using depth,” in ICCV , 2017, pp. 3828–3836
2017
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K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask R-CNN,” in ICCV , 2017, pp. 2961–2969
2017
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T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, “Focal loss for dense object detection,” in ICCV , 2017, pp. 2980–2988
2017
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Y. Xiang, T. Schmidt, V. Narayanan, and D. Fox, “PoseCNN: A convolutional neural network for 6D object pose estimation in cluttered scenes,” in RSS , 2018
2018
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J. Tremblay, T. To, B. Sundaralingam, Y. Xiang, D. Fox, and S. Birchfield, “Deep object pose estimation for semantic robotic grasping of household objects,” in CoRL , 2018, pp. 306–316
2018
Cited alongside, same era.
G. Pavlakos, X. Zhou, A. Chan, K. G. Derpanis, and K. Daniilidis, “6-DoF object pose from semantic keypoints,” in ICRA , 2017, pp. 2011–2018
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Y. Li, G. Wang, X. Ji, Y. Xiang, and D. Fox, “DeepIM: Deep iterative matching for 6D pose estimation,” in ECCV , 2018, pp. 683–698
2018
Cited alongside, same era.
M. Sundermeyer, Z.-C. Marton, M. Durner, M. Brucker, and R. Triebel, “Implicit 3D orientation learning for 6D object detection from RGB images,” in ECCV , 2018, pp. 699–715
L. Manuelli, W. Gao, P. Florence, and R. Tedrake, “kPAM: Keypoint affordances for category-level robotic manipulation,” ISRR , 2019
2019
Later among the works it cites.
L. Ke, S. Li, Y. Sun, Y.-W. Tai, and C.-K. Tang, “GSNet: Joint vehicle pose and shape reconstruction with geometrical and scene-aware supervision,” in ECCV , 2020, pp. 515–532
2020
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M. Tian, M. H. Ang, and G. H. Lee, “Shape prior deformation for categorical 6D object pose and size estimation,” in ECCV , 2020, pp. 530–546
2020
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D. Chen, J. Li, Z. Wang, and K. Xu, “Learning canonical shape space for category-level 6D object pose and size estimation,” in CVPR , 2020, pp. 11 973–11 982
2020
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X. Chen, Z. Dong, J. Song, A. Geiger, and O. Hilliges, “Category level object pose estimation via neural analysis-by-synthesis,” in ECCV , 2020, pp. 139–156
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2018
Cited alongside, same era.
T. Hodaň, F. Michel, E. Brachmann, W. Kehl, A. Glent Buch, D. Kraft, B. Drost, J. Vidal, S. Ihrke, X. Zabulis, C. Sahin, F. Manhardt, F. Tombari, T.-K. Kim, J. Matas, and C. Rother, “BOP: Benchmark for 6D object pose estimation,” ECCV , 2018
2018
Cited alongside, same era.
B. Tekin, S. N. Sinha, and P. Fua, “Real-time seamless single shot 6D object pose prediction,” in CVPR , 2018, pp. 292–301
2018
Cited alongside, same era.
M. Oberweger, M. Rad, and V. Lepetit, “Making deep heatmaps robust to partial occlusions for 3D object pose estimation,” in ECCV , 2018, pp. 119–134
2018
Cited alongside, same era.
F. Yu, D. Wang, E. Shelhamer, and T. Darrell, “Deep layer aggregation,” in CVPR , 2018, pp. 2403–2412
2018
Cited alongside, same era.
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 CVPR , 2019, pp. 3343–3352
2019
Cited alongside, same era.
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 CVPR , 2019, pp. 2642–2651
2019
Cited alongside, same era.
X. Zhou, D. Wang, and P. Krähenbühl, “Objects as points,” arXiv preprint arXiv:1904.07850 , 2019
2019
Cited alongside, same era.
2020
Later among the works it cites.
2020
Later among the works it cites.
2020
Later among the works it cites.
2020
Later among the works it cites.
C. Song, J. Song, and Q. Huang, “HybridPose: 6D object pose estimation under hybrid representations,” in CVPR , 2020, pp. 431–440
2020
Later among the works it cites.
Z. Liu, Z. Wu, and R. Tóth, “SMOKE: Single-stage monocular 3D object detection via keypoint estimation,” in CVPR Workshops , 2020, pp. 996–997
2020
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Y. Wang, Z. Xu, H. Shen, B. Cheng, and L. Yang, “CenterMask: Single shot instance segmentation with point representation,” in CVPR , 2020, pp. 9313–9321
2020
Later among the works it cites.
M. A. Lee, C. Florensa, J. Tremblay, N. Ratliff, A. Garg, F. Ramos, and D. Fox, “Guided Uncertainty-Aware Policy Optimization: Combining learning and model-based strategies for sample-efficient policy learning,” in ICRA , 2020, pp. 7505–7512
2020
Later among the works it cites.
A. Ahmadyan, L. Zhang, A. Ablavatski, J. Wei, and M. Grundmann, “Objectron: A large scale dataset of object-centric videos in the wild with pose annotations,” in CVPR , 2021, pp. 7822–7831
2021
Closest in time.
R. Xu, F.-J. Chu, C. Tang, W. Liu, and P. A. Vela, “An affordance keypoint detection network for robot manipulation,” RA-L , vol. 6, no. 2, pp. 2870–2877, 2021
2021
Closest in time.