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This paper addresses the task of estimating the 6 degrees of freedom pose of a known 3D object from depth information represented by a point cloud.
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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 ACCV , 2012
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R. Hartley, J. Trumpf, Y. Dai, and H. Li, “Rotation averaging,” International Journal of Computer Vision , vol. 103, no. 3, pp. 267–305, 2013
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A. Tejani, D. Tang, R. Kouskouridas, and T. K. Kim, “Latent-class hough forests for 3D object detection and pose estimation,” in ECCV , 2014
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E. Brachmann, A. Krull, F. Michel, J. S. S. Gumhold, and C. Rother, “Learning 6D object pose estimation using 3D object coordinates,” in ECCV , 2014
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A. Krull, E. Brachmann, F. Michel, M. Y. Yang, S. Gumhold, and C. Rother, “Learning analysis-by-synthesis for 6D pose estimation in RGB-D images,” in ICCV , 2015
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A. Kendall, M. Grimes, and R. Cipolla, “PoseNet: a convolutional network for real-time 6-DOF camera relocalization,” in ICCV , 2015
2015
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W. Kehl, F. Milletari, F. Tombari, S. Ilic, and N. Navab, “Deep learning of local RGB-D pacthes for 3D object detection and 6D pose estimation,” in ECCV , 2016
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S. Hinterstoisser, V. Lepetit, N. Rajkumar, and K. Konolige, “Going further with point pair features,” in ECCV , 2016
2016
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A. Doumanoglou, R. Kouskouridas, S. Malassiotis, and T. K. Kim, “Recovering 6D object pose and predicting next-best-view in the crowd,” in CVPR , 2016
2016
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W. Kehl, F. Manhardt, F. Tombari, S. Ilic, and N. Navab, “SSD-6D: making RGB-based 3D detection and 6D pose estimation great again,” in ICCV , 2017
2017
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S. Zakharov, W. Kehl, B. Planche, A. Hutter, and S. Ilic, “3D object instance recognition and pose estimation using triplet loss with dynamic margin,” in IROS , 2017
M. Oberweger, M. Rad, and V. Lepetit, “Making deep heatmaps robust to partial occlusions for 3D object pose estimation,” in ECCV , 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
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C. Li, J. Bai, and G. D. Hager, “A unified framework for multi-view multi-class object pose estimation,” in ECCV , 2018
2018
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M. Bui, S. Zakharov, S. Albarqouni, S. Ilic, and N. Navab, “When regression meets manifold learning for object recognition and pose estimation,” in ICRA , 2018
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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
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2017
Cited alongside, same era.
C. Sahin, R. Kouskouridas, and T. Kim, “A learning-based variable size part extraction architecture for 6D object pose recovery in depth images,” Image and Vision Computing , vol. 63, no. C, pp. 38–50, 2017
2017
Cited alongside, same era.
C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “PointNet: Deep learning on point sets for 3D classification and segmentation,” in CVPR , 2017
2017
Cited alongside, same era.
A. Kendall and R. Cipolla, “Geometric loss functions for camera pose regression with deep learning,” in CVPR , 2017
2017
Cited alongside, same era.
F. Michel, A. Kirillov, E. Brachmann, A. Krull, S. Gumhold, B. Savchynskyy, and C. Rother, “Global hypothesis generation for 6D object pose estimation,” in CVPR , 2017
2017
Cited alongside, same era.
V. Balntas, A. Doumanoglou, C. Sahin, J. Sock, R. Kouskouridas, and T. K. Kim, “Pose guided RGBD feature learning for 3D object pose estimation,” in ICCV , 2017
2017
Cited alongside, same era.
S. Mahendran, H. Ali, and R. Vidal, “3D pose regression using convolutional neural networks,” in CVPR workshops , 2017
2017
Cited alongside, same era.
2018
Later among the works it cites.
O. H. Jafari, S. K. Mustikovela, K. Pertsch, E. Brachmann, and C. Rother, “iPose: Instance-aware 6D pose estimation of partly occluded objects,” in ACCV , 2018
2018
Later among the works it cites.
A. Tejani, R. Kouskouridas, A. Doumanoglou, D. Tang, and T. K. Kim, “Latent-class hough forests for 6 DoF object pose estimation,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 40, no. 1, pp. 119–132, 2018
2018
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C. R. Qi, W. Liu, C. Wu, H. Su, and L. J. Guibas, “Frustum PointNets for 3D object detection from RGB-D data,” in CVPR , 2018
2018
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G. Gao, M. Lauri, J. Zhang, and S. Frintrop, “Occlusion resistant object rotation regression from point cloud segments,” in ECCV 4th International Workshop on Recovering 6D Object Pose , 2018
2018
Later among the works it cites.
2018
Later among the works it 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 CVPR , 2019
2019
Later among the works it cites.