Consensus disagreement among off-the-shelf 6-DoF pose estimators, encoded as signed 6D differences, lets a lightweight MLP predict simulated grasp success more accurately than an ADD-based uncertainty baseline.
A review on object pose recovery: From 3D bounding box detectors to full 6D pose estimators,
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Consensus-Driven Uncertainty for Robotic Grasping based on RGB Perception
Consensus disagreement among off-the-shelf 6-DoF pose estimators, encoded as signed 6D differences, lets a lightweight MLP predict simulated grasp success more accurately than an ADD-based uncertainty baseline.