Fetching the paper…
Reading the bibliography…
Accurate 6D object pose estimation is fundamental to robotic manipulation and grasping.
D. G. Lowe et al. , “Object recognition from local scale-invariant features.” in iccv , vol. 99, 1999, pp. 1150–1157
1999
Earlier work this paper cites.
D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” International journal of computer vision , vol. 60, pp. 91–110, 2004
2004
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 Asian conference on computer vision . Springer, 2012, pp. 548–562
2012
Earlier work this paper cites.
Y. Yan and G. S. Chirikjian, “Almost-uniform sampling of rotations for conformational searches in robotics and structural biology,” in 2012 IEEE International Conference on Robotics and Automation . IEEE, 2012, pp. 4254–4259
2012
Earlier work this paper cites.
E. Brachmann, A. Krull, F. Michel, S. Gumhold, J. Shotton, and C. Rother, “Learning 6d object pose estimation using 3d object coordinates,” in European conference on computer vision . Springer, 2014, pp. 536–551
2014
Earlier work this paper cites.
A. Tejani, D. Tang, R. Kouskouridas, and T.-K. Kim, “Latent-class hough forests for 3d object detection and pose estimation,” in European Conference on Computer Vision . Springer, 2014, pp. 462–477
2014
Earlier work this paper cites.
A. Krull, E. Brachmann, F. Michel, M. Ying Yang, S. Gumhold, and C. Rother, “Learning analysis-by-synthesis for 6d pose estimation in rgb-d images,” in Proceedings of the IEEE International Conference on Computer Vision , 2015, pp. 954–962
2015
Earlier work this paper cites.
S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” in Advances in neural information processing systems , 2015, pp. 91–99
2015
Earlier work this paper cites.
B. Calli, A. Singh, A. Walsman, S. Srinivasa, P. Abbeel, and A. M. Dollar, “The ycb object and model set: Towards common benchmarks for manipulation research,” in 2015 international conference on advanced robotics (ICAR) . IEEE, 2015, pp. 510–517
2015
Earlier work this paper cites.
N. Correll, K. E. Bekris, D. Berenson, O. Brock, A. Causo, K. Hauser, K. Okada, A. Rodriguez, J. M. Romano, and P. R. Wurman, “Analysis and observations from the first amazon picking challenge,” IEEE Transactions on Automation Science and Engineering , vol. 15, no. 1, pp. 172–188, 2016
2016
Earlier work this paper cites.
E. Brachmann, F. Michel, A. Krull, M. Ying Yang, S. Gumhold, et al. , “Uncertainty-driven 6d pose estimation of objects and scenes from a single rgb image,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 3364–3372
2016
Earlier work this paper cites.
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg, “Ssd: Single shot multibox detector,” in European conference on computer vision . Springer, 2016, pp. 21–37
2016
Earlier work this paper cites.
W. Kehl, F. Milletari, F. Tombari, S. Ilic, and N. Navab, “Deep learning of local rgb-d patches for 3d object detection and 6d pose estimation,” in European Conference on Computer Vision . Springer, 2016, pp. 205–220
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778
2016
Earlier work this paper cites.
T. Hodaň, J. Matas, and Š. Obdržálek, “On evaluation of 6d object pose estimation,” in European Conference on Computer Vision . Springer, 2016, pp. 606–619
2016
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 Proceedings of the IEEE International Conference on Computer Vision , 2017, pp. 1521–1529
2017
Cited alongside, same era.
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 Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 652–660
2017
Cited alongside, same era.
2018
Later among the works it cites.
T. Hodan, F. Michel, E. Brachmann, W. Kehl, A. GlentBuch, D. Kraft, B. Drost, J. Vidal, S. Ihrke, X. Zabulis, et al. , “Bop: benchmark for 6d object pose estimation,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 19–34
2018
Later among the works it cites.
C. Li, J. Bai, and G. D. Hager, “A unified framework for multi-view multi-class object pose estimation,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 254–269
2018
Later among the works it cites.
D. Novotny, S. Albanie, D. Larlus, and A. Vedaldi, “Self-supervised learning of geometrically stable features through probabilistic introspection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 3637–3645
2018
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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 Proceedings of the IEEE International Conference on Computer Vision , 2017, pp. 3828–3836
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 Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 462–471
2017
Cited alongside, same era.
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 , 2017
2017
Cited alongside, same era.
K. He, G. Gkioxari, P. Dollar, and R. Girshick, “Mask r-cnn,” in The IEEE International Conference on Computer Vision (ICCV) , Oct 2017
2017
Cited alongside, same era.
A. Mousavian, D. Anguelov, J. Flynn, and J. Kosecka, “3d bounding box estimation using deep learning and geometry,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 7074–7082
2017
Cited alongside, same era.
H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia, “Pyramid scene parsing network,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 2881–2890
2017
Cited alongside, same era.
2018
Cited alongside, same era.
B. Tekin, S. N. Sinha, and P. Fua, “Real-time seamless single shot 6d object pose prediction,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 292–301
2018
Cited alongside, same era.
Later among the works it cites.
A. Dai and M. Nießner, “3dmv: Joint 3d-multi-view prediction for 3d semantic scene segmentation,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 452–468
2018
Later among the works it cites.
S. Liu, L. Qi, H. Qin, J. Shi, and J. Jia, “Path aggregation network for instance segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 8759–8768
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
Y. Li, G. Wang, X. Ji, Y. Xiang, and D. Fox, “Deepim: Deep iterative matching for 6d pose estimation,” in The European Conference on Computer Vision (ECCV) , September 2018
2018
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 Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 4561–4570
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.