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Most recent 6D object pose estimation methods, including unsupervised ones, require many real training images.
Lowe, D.G.: Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision 20
2004
Earlier work this paper cites.
Lepetit, V., Moreno-Noguer, F., Fua, P.: EPnP: An Accurate O(n) Solution to the PnP Problem. International Journal of Computer Vision (2009)
2009
Earlier work this paper cites.
Tola, E., Lepetit, V., Fua, P.: DAISY: An Efficient Dense Descriptor Applied to Wide Baseline Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence 32
2010
Earlier work this paper cites.
Hinterstoisser, S., Lepetit, V., Ilic, S., Holzer, S., Bradski, G., Konolige, K., Navab, N.: Model Based Training, Detection and Pose Estimation of Texture-Less 3D Objects in Heavily Cluttered Scenes. In: Asian Conference on Computer Vision (2012)
2012
Earlier work this paper cites.
Trzcinski, T., Christoudias, C.M., Lepetit, V., Fua, P.: Learning Image Descriptors with the Boosting-Trick. In: Advances in Neural Information Processing Systems (December 2012)
2012
Earlier work this paper cites.
Zheng, Y., Kuang, Y., Sugimoto, S., Åström, K., Okutomi, M.: Revisiting the PnP Problem: A Fast, General and Optimal Solution. In: International Conference on Computer Vision (2013)
2013
Earlier work this paper cites.
Ferraz, L., Binefa, X., Moreno-Noguer, F.: Very Fast Solution to the PnP Problem with Algebraic Outlier Rejection. In: Conference on Computer Vision and Pattern Recognition. pp. 501–508 (2014)
2014
Earlier work this paper cites.
Kneip, L., Li, H., Seo, Y.: UPnP: An Optimal O(n) Solution to the Absolute Pose Problem with Universal Applicability. In: European Conference on Computer Vision (2014)
2014
Earlier work this paper cites.
Krull, A., Brachmann, E., Michel, F., Yang, M.Y., Gumhold, S., Rother, C.: Learning Analysis-By-Synthesis for 6D Pose Estimation in RGB-D Images. In: International Conference on Computer Vision (2015)
2015
Earlier work this paper cites.
Revaud, J., Weinzaepfel, P., Harchaoui, Z., Schmid, C.: EpicFlow: Edge-Preserving Interpolation of Correspondences for Optical Flow. In: Conference on Computer Vision and Pattern Recognition (2015)
2015
Earlier work this paper cites.
Hu, Y., Song, R., Li, Y.: Efficient Coarse-to-Fine PatchMatch for Large Displacement Optical Flow. In: Conference on Computer Vision and Pattern Recognition (2016)
2016
Earlier work this paper cites.
Hu, Y., Li, Y., Song, R.: Robust Interpolation of Correspondences for Large Displacement Optical Flow. In: Conference on Computer Vision and Pattern Recognition (2017)
2017
Earlier work this paper cites.
Ilg, E., Mayer, N., Saikia, T., Keuper, M., Dosovitskiy, A., Brox, T.: FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks. In: Conference on Computer Vision and Pattern Recognition (2017)
2017
Earlier work this paper cites.
Kehl, W., Manhardt, F., Tombari, F., Ilic, S., Navab, N.: SSD-6D: Making Rgb-Based 3D Detection and 6D Pose Estimation Great Again. In: International Conference on Computer Vision (2017)
2017
Earlier work this paper cites.
Rad, M., Lepetit, V.: BB8: A Scalable, Accurate, Robust to Partial Occlusion Method for Predicting the 3D Poses of Challenging Objects Without Using Depth. In: International Conference on Computer Vision (2017)
2017
Earlier work this paper cites.
Hoda, T., Michel, F., Brachmann, E., Kehl, W., Buch, A.G., Kraft, D., Drost, B., Vidal, J., Ihrke, S., Zabulis, X., Sahin, C., Manhardt, F., Tombari, F., Kim, T.K., Matas, J., Rother, C.: BOP: Benchmark for 6D Object Pose Estimation. In: European Conference on Computer Vision (2018)
2018
Earlier work this paper cites.
Jafari, O.H., Mustikovela, S.K., Pertsch, K., Brachmann, E., Rother, C.: IPose: Instance-Aware 6D Pose Estimation of Partly Occluded Objects. In: Asian Conference on Computer Vision (2018)
2018
Earlier work this paper cites.
Kato, H., Ushiku, Y., Harada, T.: Neural 3D Mesh Renderer. In: Conference on Computer Vision and Pattern Recognition (2018)
2018
Earlier work this paper cites.
Li, Y., Wang, G., Ji, X., Xiang, Y., Fox, D.: DeepIM: Deep Iterative Matching for 6D Pose Estimation. In: European Conference on Computer Vision (2018)
2018
Cited alongside, same era.
Manhardt, F., Kehl, W., Navab, N., Tombari, F.: Deep Model-Based 6D Pose Refinement in RGB. In: European Conference on Computer Vision (2018)
2018
Cited alongside, same era.
Oberweger, M., Rad, M., Lepetit, V.: Making Deep Heatmaps Robust to Partial Occlusions for 3D Object Pose Estimation. In: European Conference on Computer Vision (2018)
2018
Cited alongside, same era.
Rad, M., Oberweger, M., Lepetit, V.: Feature Mapping for Learning Fast and Accurate 3D Pose Inference from Synthetic Images. In: Conference on Computer Vision and Pattern Recognition (2018)
2018
Cited alongside, same era.
Sun, D., Yang, X., Liu, M., Kautz, J.: PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume. In: Conference on Computer Vision and Pattern Recognition (2018)
Joshi, B., Modasshir, M., Manderson, T., Damron, H., Xanthidis, M., Li, A.Q., Rekleitis, I., Dudek, G.: DeepURL: Deep Pose Estimation Framework for Underwater Relative Localization. In: International Conference on Intelligent Robots and Systems (2020)
2020
Later among the works it cites.
Kisantal, M., Sharma, S., Park, T.H., Izzo, D., Märtens, M., D’Amico, S.: Satellite Pose Estimation Challenge: Dataset, Competition Design and Results. In: IEEE Transactions on Aerospace and Electronic Systems (2020)
2020
Later among the works it cites.
Labbé, Y., Carpentier, J., Aubry, M., Sivic, J.: CosyPose: Consistent Multi-View Multi-Object 6D Pose Estimation. In: European Conference on Computer Vision (2020)
2020
Later among the works it cites.
Pan, F., Shin, I., Rameau, F., Lee, S., Kweon, I.: Unsupervised Intra-Domain Adaptation for Semantic Segmentation through Self-Supervision. In: Conference on Computer Vision and Pattern Recognition (2020)
2020
Later among the works it cites.
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2018
Cited alongside, same era.
Tekin, B., Sinha, S.N., Fua, P.: Real-Time Seamless Single Shot 6D Object Pose Prediction. In: Conference on Computer Vision and Pattern Recognition (2018)
2018
Cited alongside, same era.
Xiang, Y., Schmidt, T., Narayanan, V., Fox, D.: PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes. In: Robotics: Science and Systems Conference (2018)
2018
Cited alongside, same era.
Hodan, T., Vineet, V., Gal, R., Shalev, E., Hanzelka, J., Connell, T., Urbina, P., Sinha, S., Guenter, B.: Photorealistic Image Synthesis for Object Instance Detection. In: International Conference on Image Processing (2019)
2019
Cited alongside, same era.
Hu, Y., Hugonot, J., Fua, P., Salzmann, M.: Segmentation-Driven 6D Object Pose Estimation. In: Conference on Computer Vision and Pattern Recognition (2019)
2019
Cited alongside, same era.
Li, Z., Wang, G., Ji, X.: CDPN: Coordinates-Based Disentangled Pose Network for Real-Time Rgb-Based 6-DoF Object Pose Estimation. In: International Conference on Computer Vision (2019)
2019
Cited alongside, same era.
Park, K., Patten, T., Vincze, M.: Pix2Pose: Pixel-Wise Coordinate Regression of Objects for 6D Pose Estimation. In: International Conference on Computer Vision (2019)
2019
Cited alongside, same era.
Peng, S., Liu, Y., Huang, Q., Zhou, X., Bao, H.: PVNet: Pixel-Wise Voting Network for 6DoF Pose Estimation. In: Conference on Computer Vision and Pattern Recognition (2019)
2019
Cited alongside, same era.
Sock, J., Garcia-Hernando, G., Armagan, A., Kim, T.K.: Introducing Pose Consistency and Warp-Alignment for Self-Supervised 6D Object Pose Estimation in Color Images. In: International Conference on 3D Vision (2020)
2020
Later among the works it cites.
Song, C., Song, J., Huang, Q.: HybridPose: 6D Object Pose Estimation Under Hybrid Representations. In: Conference on Computer Vision and Pattern Recognition (2020)
2020
Later among the works it cites.
Sundermeyer, M., Durner, M., Puang, E.Y., Marton, Z.C., Vaskevicius, N., Arras, K.O., Triebel, R.: Multi-Path Learning for Object Pose Estimation Across Domains. In: Conference on Computer Vision and Pattern Recognition (2020)
2020
Later among the works it cites.
Tang, H., Chen, K., Jia, K.: Unsupervised Domain Adaptation via Structurally Regularized Deep Clustering. In: Conference on Computer Vision and Pattern Recognition (2020)
2020
Later among the works it cites.
Teed, Z., Deng, J.: RAFT: Recurrent All-Pairs Field Transforms for Optical Flow. In: European Conference on Computer Vision (2020)
2020
Later among the works it cites.
Di, Y., Manhardt, F., Wang, G., Ji, X., Navab, N., Tombari, F.: SO-Pose: Exploiting Self-Occlusion for Direct 6D Pose Estimation. In: International Conference on Computer Vision (2021)
2021
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Gulrajani, I., Lopez-Paz, D.: In Search of Lost Domain Generalization. In: International Conference on Learning Representations (2021)
2021
Later among the works it cites.
Hu, Y., Speierer, S., Jakob, W., Fua, P., Salzmann, M.: Wide-Depth-Range 6D Object Pose Estimation in Space. In: Conference on Computer Vision and Pattern Recognition (2021)
2021
Later among the works it cites.
Risholm, P., Ivarsen, P.O., Haugholt, K.H., Mohammed, A.: Underwater Marker-Based Pose-Estimation with Associated Uncertainty. In: International Conference on Computer Vision (2021)
2021
Later among the works it cites.
Wang, G., Manhardt, F., Tombari, F., Ji, X.: GDR-Net: Geometry-Guided Direct Regression Network for Monocular 6D Object Pose Estimation. In: Conference on Computer Vision and Pattern Recognition (2021)
2021
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Wang, Z., Luo, Y., Qiu, R., Huang, Z., Baktashmotlagh, M.: Learning to Diversify for Single Domain Generalization. In: International Conference on Computer Vision (2021)
2021
Later among the works it cites.
Xu, Q., Zhang, R., Zhang, Y., Wang, Y., Tian, Q.: A Fourier-Based Framework for Domain Generalization. In: Conference on Computer Vision and Pattern Recognition (2021)
2021
Later among the works it cites.
Zhou, K., Yang, Y., Qiao, Y., Xiang, T.: Domain Generalization with Mixstyle. In: International Conference on Learning Representations (2021)
2021
Later among the works it cites.