  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 regions in the original image
as input. This is then used to calibrate the confidence scores of the pose
proposals. This new set of confidence scores is found to be significantly more
reliable for accurate object pose estimation as shown by our results. Our
experimental results on multiple challenging datasets (LINEMOD and Occlusion
LINEMOD) reflects the utility of our proposed method. Our overall pose
estimation pipeline outperforms state-of-the-art object pose estimation methods
on these standard object pose estimation datasets. Our code is publicly
available on https://github.com/kartikgupta-at-anu/CullNet.
