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arXiv · 2106.06684

Multistream ValidNet: Improving 6D Object Pose Estimation by Automatic Multistream Validation

Joy Mazumder·Mohsen Zand·Michael Greenspan
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Abstract

This work presents a novel approach to improve the results of pose estimation by detecting and distinguishing between the occurrence of True and False Positive results. It achieves this by training a binary classifier on the output of an arbitrary pose estimation algorithm, and returns a binary label indicating the validity of the result. We demonstrate that our approach improves upon a state-of-the-art pose estimation result on the Sil\'eane dataset, outperforming a variation of the alternative CullNet method by 4.15% in average class accuracy and 0.73% in overall accuracy at validation. Applying our method can also improve the pose estimation average precision results of Op-Net by 6.06% on average.

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Joy Mazumder, Mohsen Zand, Michael Greenspan. 2021-06-12. Multistream ValidNet: Improving 6D Object Pose Estimation by Automatic Multistream Validation. https://arxiv.org/abs/2106.06684

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