Fetching the paper…
Reading the bibliography…
It is often desired to train 6D pose estimation systems on synthetic data because manual annotation is expensive.
S. Katz, A. Tal, and R. Basri, “Direct visibility of point sets,” in SIGGRAPH , 2007
2007
Earlier work this paper cites.
L. van der Maaten and G. Hinton, “Visualizing data using t-sne,” Journal of Machine Learning Research , vol. 9, no. 86, pp. 2579–2605, 2008
2008
Earlier work this paper cites.
P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, P.-A. Manzagol, and L. Bottou, “Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion.” Journal of machine learning research , vol. 11, no. 12, 2010
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 ACCV , 2012
2012
Earlier work this paper cites.
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
2014
Earlier work this paper cites.
H. Su, C. R. Qi, Y. Li, and L. J. Guibas, “Render for CNN: Viewpoint estimation in images using CNNs trained with rendered 3D model views,” in ICCV , 2015
2015
Earlier work this paper cites.
B. Calli, A. Walsman, A. Singh, S. Srinivasa, and P. Abbeel, “Benchmarking in manipulation research using the Yale-CMU-Berkeley object and model set,” Robotics & Automation Magazine, IEEE , vol. 22, no. 3, pp. 36–52, 2015
2015
Earlier work this paper cites.
A. Zeng, K. 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
2017
Earlier work this paper cites.
J. Wu, L. Ma, and X. Hu, “Delving deeper into convolutional neural networks for camera relocalization,” in ICRA , 2017
2017
Earlier work this paper cites.
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
Earlier work this paper cites.
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
2017
Earlier work this paper cites.
C. Mitash, K. E. Bekris, and A. Boularias, “A self-supervised learning system for object detection using physics simulation and multi-view pose estimation,” in IROS , 2017
2017
Earlier work this paper cites.
V. Badrinarayanan, A. Kendall, and R. Cipolla, “SegNet: A deep convolutional encoder-decoder architecture for image segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 39, no. 12, pp. 2481–2495, 2017
2017
Cited alongside, same era.
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
Cited alongside, same era.
P. Marion, P. R. Florence, L. Manuelli, and R. Tedrake, “LabelFusion: A pipeline for generating ground truth labels for real RGBD data of cluttered scenes,” in ICRA , 2018
2018
Cited alongside, same era.
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
Cited alongside, same era.
S. Zakharov, I. Shugurov, and S. Ilic, “DPOD: 6D pose object detector and refiner,” in ICCV , 2019
2019
Later among the works it cites.
K. Park, T. Patten, and M. Vincze, “Pix2Pose: Pixel-wise coordinate regression of objects for 6D pose estimation,” in ICCV , 2019
2019
Later among the works it cites.
T. Hodaň, V. Vineet, R. Gal, E. Shalev, J. Hanzelka, T. Connell, P. Urbina, S. Sinha, and B. Guenter, “Photorealistic image synthesis for object instance detection,” in ICIP , 2019
2019
Later among the works it cites.
F. Manhardt, W. Kehl, and A. Gaidon, “ROI-10D: monocular lifting of 2D detection to 6D pose and metric shape,” in CVPR , 2019
2019
Later among the works it cites.
Y. Wang, Y. Sun, Z. Liu, S. E. Sarma, M. M. Bronstein, and J. M. Solomon, “Dynamic graph CNN for learning on point clouds,” ACM Transactions on Graphics (TOG) , 2019
2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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,” in ECCV , 2018
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
2018
Cited alongside, same era.
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
Cited alongside, same era.
B. Tekin, S. N. Sinha, and P. Fua, “Real-time seamless single shot 6D object pose prediction,” in CVPR , 2018
2018
Cited alongside, same era.
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
2018
Cited alongside, same era.
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
Cited alongside, same era.
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
2019
Cited alongside, same era.
Later among the works it cites.
S. Peng, Y. Liu, Q. Huang, X. Zhou, and H. Bao, “PVNet: Pixel-wise voting network for 6dof pose estimation,” in CVPR , 2019
2019
Later among the works it cites.
2019
Later among the works it cites.
T. Hodaň, M. Sundermeyer, B. Drost, Y. Labbe, E. Brachmann, F. Michel, C. Rother, J. Matas, and C. Rother, “BOP challenge 2020 on 6D object localization,” in ECCV Workshop on Recovering 6D Object Pose , 2020
2020
Later among the works it cites.
G. Gao, M. Lauri, Y. Wang, X. Hu, J. Zhang, and S. Frintrop, “6D object pose regression via supervised learning on point clouds,” in ICRA , 2020
2020
Later among the works it cites.
Y. He, W. Sun, H. Huang, J. Liu, H. Fan, and J. Sun, “PVN3D: A deep point-wise 3D keypoints voting network for 6DoF pose estimation,” in CVPR , 2020
2020
Later among the works it cites.
Y. Wen, H. Pan, L. Yang, and W. Wang, “Edge enhanced implicit orientation learning with geometric prior for 6D pose estimation,” IEEE Robotics and Automation Letters , vol. 5, no. 3, pp. 4931–4938, 2020
2020
Later among the works it cites.
F. Hagelskjær and A. Buch, “PointVoteNet: Accurate object detection and 6 DoF pose estimation in point clouds,” in ICIP , 2020
2020
Later among the works it cites.