Abstract. Modern deep learning systems successfully solve many perception tasks such as object pose estimation when the input image is
of high quality. However, in challenging imaging conditions such as on
low resolution images or when the image is corrupted by imaging artifacts, current systems degrade considerably in accuracy. While a loss
in performance is unavoidable, we would like our models to quantify
their uncertainty to achieve robustness against images of varying quality.
Probabilistic deep learning models combine the expressive power of deep
learning with uncertainty quantification. In this paper we propose a novel
probabilistic deep learning model for the task of angular regression. Our
model uses von Mises distributions to predict a distribution over object
pose angle. Whereas a single von Mises distribution is making strong
assumptions about the shape of the distribution, we extend the basic
model to predict a mixture of von Mises distributions. We show how
to learn a mixture model using a finite and infinite number of mixture
components. Our model allows for likelihood-based training and efficient
inference at test time. We demonstrate on a number of challenging pose
estimation datasets that our model produces calibrated probability predictions and competitive or superior point estimates compared to the
current state-of-the-art