/PoseRefinement

Reviewing some 6D object pose refinement techniques

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PoseRefinement

A Review of 6D object pose refinement techniques

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References -

  1. Li, Yi, et al. "Deepim: Deep iterative matching for 6d pose estimation." Proceedings of the European Conference on Computer Vision (ECCV). 2018.
  2. DeepIM implementation - https://github.com/NVlabs/DeepIM-PyTorch
  3. Pereira, Nuno, and Luís A. Alexandre. "Maskedfusion: mask-based 6d object pose estimation." 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2020.
  4. Xu, D., Anguelov, D., Jain, A.: Pointfusion: Deep sensor fusion for 3d bounding box estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 244–253 (2018)
  5. Wang, Chen, et al. "Densefusion: 6d object pose estimation by iterative dense fusion." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019.
  6. Peng, S., Liu, Y., Huang, Q., Zhou, X., & Bao, H. (2019). PVNet: Pixel-wise Voting Network for 6DoF Pose Estimation. In CVPR.
  7. Kiru Park and Timothy Patten and Markus Vincze (2019). Pix2Pose: Pixel-Wise Coordinate Regression of Objects for 6D Pose Estimation. CoRR, abs/1908.07433. Xingyu Liu and Rico Jonschkowski and Anelia Angelova and Kurt Konolige (2019). KeyPose: Multi-view 3D Labeling and Keypoint Estimation for Transparent Objects. CoRR, abs/1912.02805.
  8. Jonathan Tremblay and Thang To and Balakumar Sundaralingam and Yu Xiang and Dieter Fox and Stan Birchfield (2018). Deep Object Pose Estimation for Semantic Robotic Grasping of Household Objects. CoRR, abs/1809.10790.
  9. Hinterstoisser, S., Holzer, S., Cagniart, C., Ilic, S., Konolige, K., Navab, N., Lepetit, V.: Multimodal templates for real-time detection of texture-less objects in heavily cluttered scenes. In: 2011 international conference on computer vision. pp. 858–865. IEEE (2011)
  10. Xiang, Y., Schmidt, T., Narayanan, V., Fox, D.: Posecnn: A convolutional neural network for 6d object pose estimation in cluttered scenes. arXiv preprint arXiv:1711.00199 (2017)
  11. Tremblay, Jonathan, et al. "Deep object pose estimation for semantic robotic grasping of household objects." arXiv preprint arXiv:1809.10790 (2018).
  12. ICP Point to Point: https://github.com/ClayFlannigan/icp
  13. Qian-Yi Zhou, Jaesik Park, & Vladlen Koltun (2018). Open3D: A Modern Library for 3D Data Processing. arXiv:1801.09847.
  14. Badrinarayanan, Vijay, et al. “SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation.” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 12, 2017, pp. 2481–95, arxiv.org/abs/1511.00561.
  15. Y. Zheng, Y. Kuang, S. Sugimoto, K. Åström and M. Okutomi, "Revisiting the PnP Problem: A Fast, General and Optimal Solution," 2013 IEEE International Conference on Computer Vision, 2013, pp. 2344-2351, doi: 10.1109/ICCV.2013.291.