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• W4285048975 abstract "6D object pose estimation is an essential task in vision-based robotic grasping and manipulation. Prior works always train models with a large number of pose annotated images, limiting the efficiency of model transfer between different scenarios. This letter presents an end-to-end model named <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>Weak6D</i> , which could be learned with unannotated RGB-D data. The core of the proposed approach is the novel optimizing method Iterative Annotation Resolver, which has the ability to directly utilize the captured RGB-D data through the training process. Furthermore, we employ a weak refinement loss to optimize the pose estimation network with refined object poses. We evaluated the proposed <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>Weak6D</i> in the YCB-Video dataset, and experimental results show our model achieved practical results without annotated data." @default.
• W4285048975 created "2022-07-13" @default.
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• W4285048975 date "2023-03-01" @default.
• W4285048975 modified "2023-09-24" @default.
• W4285048975 title "Weak6D: Weakly Supervised 6D Pose Estimation With Iterative Annotation Resolver" @default.
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• W4285048975 doi "https://doi.org/10.1109/lra.2022.3190094" @default.
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