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• W3101552398 abstract "Deep learning-based object pose estimators are often unreliable and overconfident especially when the input image is outside the training domain, for instance, with sim2real transfer. Efficient and robust uncertainty quantification (UQ) in pose estimators is critically needed in many robotic tasks. In this work, we propose a simple, efficient, and plug-and-play UQ method for 6-DoF object pose estimation. We ensemble 2-3 pre-trained models with different neural network architectures and/or training data sources, and compute their average pairwise disagreement against one another to obtain the uncertainty quantification. We propose four disagreement metrics, including a learned metric, and show that the average distance (ADD) is the best learning-free metric and it is only slightly worse than the learned metric, which requires labeled target data. Our method has several advantages compared to the prior art: 1) our method does not require any modification of the training process or the model inputs; and 2) it needs only one forward pass for each model. We evaluate the proposed UQ method on three tasks where our uncertainty quantification yields much stronger correlations with pose estimation errors than the baselines. Moreover, in a real robot grasping task, our method increases the grasping success rate from 35% to 90%." @default.
• W3101552398 created "2020-11-23" @default.
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• W3101552398 date "2020-11-16" @default.
• W3101552398 modified "2023-09-27" @default.
• W3101552398 title "Fast Uncertainty Quantification for Deep Object Pose Estimation" @default.
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• W3101552398 doi "https://doi.org/10.48550/arxiv.2011.07748" @default.
• W3101552398 hasPublicationYear "2020" @default.
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