  Uncertainty estimation is an important task for critical problems, such as
robotics and autonomous driving, because it allows creating statistically
better perception models and signaling the model's certainty in its predictions
to the decision method or a human supervisor. In this paper, we propose a
Variational Neural Network-based version of a Voxel Pseudo Image Tracking
(VPIT) method for 3D Single Object Tracking. The Variational Feature Generation
Network of the proposed Variational VPIT computes features for target and
search regions and the corresponding uncertainties, which are later combined
using an uncertainty-aware cross-correlation module in one of two ways: by
computing similarity between the corresponding uncertainties and adding it to
the regular cross-correlation values, or by penalizing the uncertain feature
channels to increase influence of the certain features. In experiments, we show
that both methods improve tracking performance, while penalization of uncertain
features provides the best uncertainty quality.
