  6D Object pose estimation is a fundamental component in robotics enabling
efficient interaction with the environment. It is particularly challenging in
bin-picking applications, where many objects are low-feature and reflective,
and self-occlusion between objects of the same type is common. We propose a
novel multi-view approach leveraging known camera transformations from an
eye-in-hand setup to combine heatmap and keypoint estimates into a probability
density map over 3D space. The result is a robust approach that is scalable in
the number of views. It relies on a confidence score composed of keypoint
probabilities and point-cloud alignment error, which allows reliable rejection
of false positives. We demonstrate an average pose estimation error of
approximately 0.5mm and 2 degrees across a variety of difficult low-feature and
reflective objects in the ROBI dataset, while also surpassing the state-of-art
correct detection rate, measured using the 10% object diameter threshold on ADD
error.
