EA-Repose: Efficient and Accurate Feature-Metric-Based 6D Object Pose Refinement via Deep Reinforcement Learning
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-44981-9_3
Reference37 articles.
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2. Chen, H., Wang, P., Wang, F., Tian, W., Xiong, L., Li, H.: EPro-PnP: generalized end-to-end probabilistic perspective-n-points for monocular object pose estimation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2781–2790 (2022)
3. Su, Y., et al.: ZebraPose: coarse to fine surface encoding for 6DoF object pose estimation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6738–6748 (2022)
4. He, Y., Huang, H., Fan, H., Chen, Q., Sun, J.: Ffb6d: A full flow bidirectional fusion network for 6d pose estimation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3003–3013 (2021)
5. Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981)
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