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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2305.15873 |
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| _version_ | 1866910400620003328 |
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| author | Hsiao, Tsu-Ching Chen, Hao-Wei Yang, Hsuan-Kung Lee, Chun-Yi |
| author_facet | Hsiao, Tsu-Ching Chen, Hao-Wei Yang, Hsuan-Kung Lee, Chun-Yi |
| contents | Addressing pose ambiguity in 6D object pose estimation from single RGB images presents a significant challenge, particularly due to object symmetries or occlusions. In response, we introduce a novel score-based diffusion method applied to the $SE(3)$ group, marking the first application of diffusion models to $SE(3)$ within the image domain, specifically tailored for pose estimation tasks. Extensive evaluations demonstrate the method's efficacy in handling pose ambiguity, mitigating perspective-induced ambiguity, and showcasing the robustness of our surrogate Stein score formulation on $SE(3)$. This formulation not only improves the convergence of denoising process but also enhances computational efficiency. Thus, we pioneer a promising strategy for 6D object pose estimation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_15873 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Confronting Ambiguity in 6D Object Pose Estimation via Score-Based Diffusion on SE(3) Hsiao, Tsu-Ching Chen, Hao-Wei Yang, Hsuan-Kung Lee, Chun-Yi Computer Vision and Pattern Recognition Addressing pose ambiguity in 6D object pose estimation from single RGB images presents a significant challenge, particularly due to object symmetries or occlusions. In response, we introduce a novel score-based diffusion method applied to the $SE(3)$ group, marking the first application of diffusion models to $SE(3)$ within the image domain, specifically tailored for pose estimation tasks. Extensive evaluations demonstrate the method's efficacy in handling pose ambiguity, mitigating perspective-induced ambiguity, and showcasing the robustness of our surrogate Stein score formulation on $SE(3)$. This formulation not only improves the convergence of denoising process but also enhances computational efficiency. Thus, we pioneer a promising strategy for 6D object pose estimation. |
| title | Confronting Ambiguity in 6D Object Pose Estimation via Score-Based Diffusion on SE(3) |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2305.15873 |