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CullNet: Calibrated and Pose Aware Confidence Scores for Object Pose Estimation
[article]
2019
arXiv
pre-print
We present a new approach for a single view, image-based object pose estimation. Specifically, the problem of culling false positives among several pose proposal estimates is addressed in this paper. Our proposed approach targets the problem of inaccurate confidence values predicted by CNNs which is used by many current methods to choose a final object pose prediction. We present a network called CullNet, solving this task. CullNet takes pairs of pose masks rendered from a 3D model and cropped
arXiv:1909.13476v1
fatcat:mhhtvg275vc5bdh2syxhd2k7uq