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This paper studies category-level object pose estimation based on a single monocular image.
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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: Proc. of the IEEE International Conf. on Computer Vision (ICCV) (2015)
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Muñoz, E., Konishi, Y., Murino, V., Del Bue, A.: Fast 6d pose estimation for texture-less objects from a single rgb image. In: Proc. IEEE International Conf. on Robotics and Automation (ICRA) (2016)
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Zhu, J., Krähenbühl, P., Shechtman, E., Efros, A.A.: Generative visual manipulation on the natural image manifold. In: Proc. of the European Conf. on Computer Vision (ECCV) (2016)
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Krull, A., Brachmann, E., Nowozin, S., Michel, F., Shotton, J., Rother, C.: Poseagent: Budget-constrained 6d object pose estimation via reinforcement learning. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) (2017)
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Park, K., Patten, T., Vincze, M.: Pix2pose: Pixel-wise coordinate regression of objects for 6d pose estimation. In: Proc. of the IEEE International Conf. on Computer Vision (ICCV) (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: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) (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: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) (2019)
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Chen, D., Li, J., Wang, Z., Xu, K.: Learning canonical shape space for category-level 6d object pose and size estimation. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) (2020)
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Do, T., Pham, T., Cai, M., Reid, I.: Lienet: Real-time monocular object instance 6d pose estimation. In: Proc. of the British Machine Vision Conf. (BMVC) (2018)
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Li, Y., Wang, G., Ji, X., Xiang, Y., Fox, D.: Deepim: Deep iterative matching for 6d pose estimation. In: Proc. of the European Conf. on Computer Vision (ECCV) (2018)
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Sahin, C., Kim, T.: Category-level 6d object pose recovery in depth images. In: Leal-Taixé, L., Roth, S. (eds.) Proc. of the European Conf. on Computer Vision (ECCV) Workshops (2018)
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Chen, X., Dong, Z., Song, J., Geiger, A., Hilliges, O.: Category level object pose estimation via neural analysis-by-synthesis. In: Proc. of the European Conf. on Computer Vision (ECCV) (2020)
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Liao, Y., Schwarz, K., Mescheder, L.M., Geiger, A.: Towards unsupervised learning of generative models for 3d controllable image synthesis. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) (2020)
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Chen, W., Jia, X., Chang, H.J., Duan, J., Shen, L., Leonardis, A.: Fs-net: Fast shape-based network for category-level 6d object pose estimation with decoupled rotation mechanism. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) (2021)
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Duggal, S., Wang, Z., Ma, W.C., Manivasagam, S., Liang, J., Wang, S., Urtasun, R.: Secrets of 3d implicit object shape reconstruction in the wild. arXiv.org
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