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Object pose estimation is important for object manipulation and scene understanding.
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Hodan, T., Haluza, P., Obdržálek, Š., Matas, J., Lourakis, M., Zabulis, X.: T-less: An rgb-d dataset for 6d pose estimation of texture-less objects. In: 2017 IEEE Winter Conference on Applications of Computer Vision. pp. 880–888. IEEE (2017)
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Patten, T., Park, K., Vincze, M.: Dgcm-net: dense geometrical correspondence matching network for incremental experience-based robotic grasping. Frontiers in Robotics and AI 7
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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: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 13916–13925 (2020)
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Caron, M., Touvron, H., Misra, I., Jégou, H., Mairal, J., Bojanowski, P., Joulin, A.: Emerging properties in self-supervised vision transformers. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 9650–9660 (2021)
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2018
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Parmar, N., Vaswani, A., Uszkoreit, J., Kaiser, L., Shazeer, N., Ku, A., Tran, D.: Image transformer. In: International conference on machine learning. pp. 4055–4064. PMLR (2018)
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Sundermeyer, M., Marton, Z.C., Durner, M., Brucker, M., Triebel, R.: Implicit 3d orientation learning for 6d object detection from rgb images. In: Proceedings of the european conference on computer vision (ECCV). pp. 699–715 (2018)
2018
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Park, K., Patten, T., Vincze, M.: Pix2pose: Pix2pose: Pixel-wise coordinate regression of objects for 6d pose estimation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (Oct 2019)
2019
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Peng, S., Liu, Y., Huang, Q., Zhou, X., Bao, H.: Pvnet: Pixel-wise voting network for 6dof pose estimation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2019)
2019
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Wang, H., Sridhar, S., Huang, J., Valentin, J., Song, S., Guibas, L.J.: 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. pp. 2642–2651 (2019)
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2019
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Zhang, X., Jiang, Z., Zhang, H.: Real-time 6d pose estimation from a single rgb image. Image and Vision Computing 89
2019
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Jiang, Z., Wang, X., Huang, X., Li, H.: Triangulate geometric constraint combined with visual-flow fusion network for accurate 6dof pose estimation. Image and Vision Computing 108
2021
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Thalhammer, S., Leitner, M., Patten, T., Vincze, M.: Pyrapose: Feature pyramids for fast and accurate object pose estimation under domain shift. In: 2021 IEEE International Conference on Robotics and Automation (ICRA). pp. 13909–13915. IEEE (2021)
2021
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Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., Jégou, H.: Training data-efficient image transformers & distillation through attention. In: International conference on machine learning. pp. 10347–10357. PMLR (2021)
2021
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Wang, G., Manhardt, F., Tombari, F., Ji, X.: Gdr-net: Geometry-guided direct regression network for monocular 6d object pose estimation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 16611–16621 (2021)
2021
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Aing, L., Lie, W.N., Lin, G.S.: Faster and finer pose estimation for multiple instance objects in a single rgb image. Image and Vision Computing 130
2022
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Dede, M.A., Genc, Y.: Object aspect classification and 6dof pose estimation. Image and Vision Computing 124
2022
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Huang, L., Hodan, T., Ma, L., Zhang, L., Tran, L., Twigg, C., Wu, P.C., Yuan, J., Keskin, C., Wang, R.: Neural correspondence field for object pose estimation. In: Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part X. pp. 585–603. Springer (2022)
2022
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Labbé, Y., Manuelli, L., Mousavian, A., Tyree, S., Birchfield, S., Tremblay, J., Carpentier, J., Aubry, M., Fox, D., Sivic, J.: Megapose: 6d pose estimation of novel objects via render & compare. In: 6th Annual Conference on Robot Learning (2022)
2022
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Nguyen, V.N., Hu, Y., Xiao, Y., Salzmann, M., Lepetit, V.: Templates for 3d object pose estimation revisited: Generalization to new objects and robustness to occlusions. In: Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) (2022)
2022
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Shugurov, I., Li, F., Busam, B., Ilic, S.: Osop: A multi-stage one shot object pose estimation framework. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 6835–6844 (2022)
2022
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Sun, H., Wang, T., Yu, E.: A dynamic keypoint selection network for 6dof pose estimation. Image and Vision Computing 118
2022
Later among the works it cites.
Remus, A., D’Avella, S., Felice, F.D., Tripicchio, P., Avizzano, C.A.: i2c-net: Using instance-level neural networks for monocular category-level 6d pose estimation. IEEE Robotics and Automation Letters 8
2023
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2023
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Thalhammer, S., Patten, T., Vincze, M.: Cope: End-to-end trainable constant runtime object pose estimation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 2860–2870 (2023)
2023
Closest in time.