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Estimating the 3D pose of an object is a challenging task that can be considered within augmented reality or robotic applications.
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2017
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2018
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2018
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Y. Xiang, T. Schmidt, V. Narayanan, and D. Fox, “PoseCNN: A convolutional neural network for 6D object pose estimation in cluttered scenes,” in Robotics: Science and Systems (RSS) , 2018
2018
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2018
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2019
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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 IEEE Conf. on Computer Vision and Pattern Recognition , 2019, pp. 4561–4570
2019
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Z. Li, G. Wang, and X. Ji, “CDPN: Coordinates-Based Disentangled Pose Network for real-time RGB-Based 6-DoF object pose estimation,” in IEEE Int. Conf. on Computer Vision , 2019, pp. 7678–7687
2019
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K. Park, T. Patten, and M. Vincze, “Pix2Pose: Pixel-wise coordinate regression of objects for 6D pose estimation,” in IEEE Int. Conf. on Computer Vision , 2019, pp. 7668–7677
2019
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2019
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Y. He, W. Sun, H. Huang, J. Liu, H. Fan, and J. Sun, “Pvn3d: A deep point-wise 3d keypoints voting network for 6dof pose estimation,” in IEEE Conf. on Computer Vision and Pattern Recognition , 2020, pp. 11 632–11 641
2020
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