  For robots to operate robustly in the real world, they should be aware of
their uncertainty. However, most methods for object pose estimation return a
single point estimate of the object's pose. In this work, we propose two
learned methods for estimating a distribution over an object's orientation. Our
methods take into account both the inaccuracies in the pose estimation as well
as the object symmetries. Our first method, which regresses from deep learned
features to an isotropic Bingham distribution, gives the best performance for
orientation distribution estimation for non-symmetric objects. Our second
method learns to compare deep features and generates a non-parameteric
histogram distribution. This method gives the best performance on objects with
unknown symmetries, accurately modeling both symmetric and non-symmetric
objects, without any requirement of symmetry annotation. We show that both of
these methods can be used to augment an existing pose estimator. Our evaluation
compares our methods to a large number of baseline approaches for uncertainty
estimation across a variety of different types of objects.
