OmniPose6D: Towards Short-Term Object Pose Tracking in Dynamic Scenes from Monocular RGB
Fuente:
arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866918110718590976 |
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| author | Lin, Yunzhi Zhao, Yipu Chu, Fu-Jen Chen, Xingyu Wang, Weiyao Tang, Hao Vela, Patricio A. Feiszli, Matt Liang, Kevin |
| author_facet | Lin, Yunzhi Zhao, Yipu Chu, Fu-Jen Chen, Xingyu Wang, Weiyao Tang, Hao Vela, Patricio A. Feiszli, Matt Liang, Kevin |
| contents | To address the challenge of short-term object pose tracking in dynamic environments with monocular RGB input, we introduce a large-scale synthetic dataset OmniPose6D, crafted to mirror the diversity of real-world conditions. We additionally present a benchmarking framework for a comprehensive comparison of pose tracking algorithms. We propose a pipeline featuring an uncertainty-aware keypoint refinement network, employing probabilistic modeling to refine pose estimation. Comparative evaluations demonstrate that our approach achieves performance superior to existing baselines on real datasets, underscoring the effectiveness of our synthetic dataset and refinement technique in enhancing tracking precision in dynamic contexts. Our contributions set a new precedent for the development and assessment of object pose tracking methodologies in complex scenes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_06694 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | OmniPose6D: Towards Short-Term Object Pose Tracking in Dynamic Scenes from Monocular RGB Lin, Yunzhi Zhao, Yipu Chu, Fu-Jen Chen, Xingyu Wang, Weiyao Tang, Hao Vela, Patricio A. Feiszli, Matt Liang, Kevin Computer Vision and Pattern Recognition Robotics To address the challenge of short-term object pose tracking in dynamic environments with monocular RGB input, we introduce a large-scale synthetic dataset OmniPose6D, crafted to mirror the diversity of real-world conditions. We additionally present a benchmarking framework for a comprehensive comparison of pose tracking algorithms. We propose a pipeline featuring an uncertainty-aware keypoint refinement network, employing probabilistic modeling to refine pose estimation. Comparative evaluations demonstrate that our approach achieves performance superior to existing baselines on real datasets, underscoring the effectiveness of our synthetic dataset and refinement technique in enhancing tracking precision in dynamic contexts. Our contributions set a new precedent for the development and assessment of object pose tracking methodologies in complex scenes. |
| title | OmniPose6D: Towards Short-Term Object Pose Tracking in Dynamic Scenes from Monocular RGB |
| topic | Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2410.06694 |