NVIDIA Research: Fast Uncertainty Quantification for Deep Object Pose Estimation – NVIDIA Technical Blog News and tutorials for developers, data scientists, and IT admins 2025-04-03T18:49:37Z http://www.open-lab.net/blog/feed/ Yuke Zhu https://github.com/yukezhu <![CDATA[NVIDIA Research: Fast Uncertainty Quantification for Deep Object Pose Estimation]]> http://www.open-lab.net/blog/?p=32708 2023-03-14T18:48:21Z 2021-06-08T19:17:25Z Researchers from NVIDIA, University of Texas at Austin and Caltech developed a simple, efficient, and plug-and-play uncertainty quantification method for the...]]> Researchers from NVIDIA, University of Texas at Austin and Caltech developed a simple, efficient, and plug-and-play uncertainty quantification method for the...

Researchers from NVIDIA, University of Texas at Austin and Caltech developed a simple, efficient, and plug-and-play uncertainty quantification method for the 6-DoF (degrees of freedom) object pose estimation task, using an ensemble of K pre-trained estimators with different architectures and/or training data sources. The researchers presented their paper ��Fast Uncertainty Quantification��

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