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Determining the relative pose of a previously unseen object between two images is pivotal to the success of generalizable object pose estimation.
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2018
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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
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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 Proceedings of the IEEE conference on computer vision and pattern recognition , 2019, pp. 3343–3352
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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 Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 2642–2651
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2019
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J. Zhang, D. Sun, Z. Luo, A. Yao, L. Zhou, T. Shen, Y. Chen, L. Quan, and H. Liao, “Learning two-view correspondences and geometry using order-aware network,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 5845–5854
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Y. Wang and J. M. Solomon, “Deep closest point: Learning representations for point cloud registration,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 3523–3532
2019
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2022
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Y. Su, M. Saleh, T. Fetzer, J. Rambach, N. Navab, B. Busam, D. Stricker, and F. Tombari, “Zebrapose: Coarse to fine surface encoding for 6dof object pose estimation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 6738–6748
2022
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2022
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W. Goodwin, S. Vaze, I. Havoutis, and I. Posner, “Zero-shot category-level object pose estimation,” in Proceedings of the European Conference on Computer Vision . Springer, 2022, pp. 516–532
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2019
Cited alongside, same era.
P.-E. Sarlin, D. DeTone, T. Malisiewicz, and A. Rabinovich, “Superglue: Learning feature matching with graph neural networks,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 4938–4947
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 Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 11 973–11 982
2020
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X. Wei, Y. Zhang, Z. Li, Y. Fu, and X. Xue, “Deepsfm: Structure from motion via deep bundle adjustment,” in Proceedings of the European Conference on Computer Vision . Springer, 2020, pp. 230–247
2020
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2020
Cited alongside, same era.
Y. Hu, P. Fua, W. Wang, and M. Salzmann, “Single-stage 6d object pose estimation,” in Proceedings of the IEEE conference on Computer Vision and Pattern Recognition , 2020, pp. 2930–2939
2020
Cited alongside, same era.
2020
Cited alongside, same era.
J. Sun, Z. Shen, Y. Wang, H. Bao, and X. Zhou, “Loftr: Detector-free local feature matching with transformers,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 8922–8931
2021
Cited alongside, same era.
2021
Cited alongside, same era.
2022
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
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L. Yao, J. Han, X. Liang, D. Xu, W. Zhang, Z. Li, and H. Xu, “Detclipv2: Scalable open-vocabulary object detection pre-training via word-region alignment,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 23 497–23 506
2023
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M. Deitke, D. Schwenk, J. Salvador, L. Weihs, O. Michel, E. VanderBilt, L. Schmidt, K. Ehsani, A. Kembhavi, and A. Farhadi, “Objaverse: A universe of annotated 3d objects,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 13 142–13 153
2023
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J. Wang, C. Rupprecht, and D. Novotny, “Posediffusion: Solving pose estimation via diffusion-aided bundle adjustment,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 9773–9783
2023
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V. N. Nguyen, T. Groueix, G. Ponimatkin, V. Lepetit, and T. Hodan, “Cnos: A strong baseline for cad-based novel object segmentation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops , 2023, pp. 2134–2140
2023
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2023
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2023
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P. Weinzaepfel, T. Lucas, V. Leroy, Y. Cabon, V. Arora, R. Brégier, G. Csurka, L. Antsfeld, B. Chidlovskii, and J. Revaud, “Croco v2: Improved cross-view completion pre-training for stereo matching and optical flow,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 17 969–17 980
2023
Later among the works it cites.
B. Wen, W. Yang, J. Kautz, and S. Birchfield, “Foundationpose: Unified 6d pose estimation and tracking of novel objects,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 17 868–17 879
2024
Closest in time.
J. Lee, Y. Cabon, R. Brégier, S. Yoo, and J. Revaud, “Mfos: Model-free & one-shot object pose estimation,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 38, no. 4, 2024, pp. 2911–2919
2024
Closest in time.
C. Zhao, T. Zhang, Z. Dang, and M. Salzmann, “Dvmnet: Computing relative pose for unseen objects beyond hypotheses,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 20 485–20 495
2024
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2024
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J. Lin, L. Liu, D. Lu, and K. Jia, “Sam-6d: Segment anything model meets zero-shot 6d object pose estimation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 27 906–27 916
2024
Closest in time.
C. Zhao, Y. Hu, and M. Salzmann, “Locposenet: Robust location prior for unseen object pose estimation,” in 2024 International Conference on 3D Vision (3DV) . IEEE, 2024, pp. 1072–1081
2024
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