  Object pose estimation is a core computer vision problem and often an
essential component in robotics. Pose estimation is usually approached by
seeking the single best estimate of an object's pose, but this approach is
ill-suited for tasks involving visual ambiguity. In such cases it is desirable
to estimate the uncertainty as a pose distribution to allow downstream tasks to
make informed decisions. Pose distributions can have arbitrary complexity which
motivates estimating unparameterized distributions, however, until now they
have only been used for orientation estimation on SO(3) due to the difficulty
in training on and normalizing over SE(3). We propose a novel method for pose
distribution estimation on SE(3). We use a hierarchical grid, a pyramid, which
enables efficient importance sampling during training and sparse evaluation of
the pyramid at inference, allowing real time 6D pose distribution estimation.
Our method outperforms state-of-the-art methods on SO(3), and to the best of
our knowledge, we provide the first quantitative results on pose distribution
estimation on SE(3). Code will be available at spyropose.github.io
