A model-agnostic pipeline estimates where a pose estimator makes errors in pose and occlusion space, then synthesizes targeted training images, improving bin-picking pose accuracy by up to 20%.
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Targeted Hard Sample Synthesis Based on Estimated Pose and Occlusion Error for Improved Object Pose Estimation
A model-agnostic pipeline estimates where a pose estimator makes errors in pose and occlusion space, then synthesizes targeted training images, improving bin-picking pose accuracy by up to 20%.