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Pose Proposal Criti...
Pose Proposal Critic: Robust Pose Refinement by Learning Reprojection Errors
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- Brynte, Lucas, 1990 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Kahl, Fredrik, 1972 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology
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(creator_code:org_t)
- 2020
- 2020
- Engelska.
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Ingår i: 31st British Machine Vision Conference, BMVC 2020.
- Relaterad länk:
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https://www.bmvc2020...
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https://research.cha... (primary) (free)
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Abstract
Ämnesord
Stäng
- In recent years, considerable progress has been made for the task of rigid object pose estimation from a single RGB-image, but achieving robustness to partial occlusions remains a challenging problem. Pose refinement via rendering has shown promise in order to achieve improved results, in particular, when data is scarce. In this paper we focus our attention on pose refinement, and show how to push the state-of-the-art further in the case of partial occlusions. The proposed pose refinement method leverages on a simplified learning task, where a CNN is trained to estimate the reprojection error between an observed and a rendered image. We experiment by training on purely synthetic data as well as a mixture of synthetic and real data. Current state-of-the-art results are outperformed for two out of three metrics on the Occlusion LINEMOD benchmark, while performing on-par for the final metric.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
Nyckelord
- Partial Occlusion
- Rendering
- Rigid Object Pose Estimation
- Pose Refinement
Publikations- och innehållstyp
- kon (ämneskategori)
- ref (ämneskategori)