  In recent years, a deep learning framework has been widely used for object
pose estimation. While quaternion is a common choice for rotation
representation of 6D pose, it cannot represent an uncertainty of the
observation. In order to handle the uncertainty, Bingham distribution is one
promising solution because this has suitable features, such as a smooth
representation over SO(3), in addition to the ambiguity representation.
However, it requires the complex computation of the normalizing constants. This
is the bottleneck of loss computation in training neural networks based on
Bingham representation. As such, we propose a fast-computable and
easy-to-implement loss function for Bingham distribution. We also show not only
to examine the parametrization of Bingham distribution but also an application
based on our loss function.
