Deep Directional Statistics: Pose Estimation with Uncertainty Quantification
This addresses the need for robust uncertainty quantification in pose estimation for applications like robotics or autonomous systems, though it is incremental as it builds on existing probabilistic deep learning methods.
The paper tackles the problem of object pose estimation under challenging imaging conditions by proposing a probabilistic deep learning model that uses von Mises distributions and mixtures to quantify uncertainty, demonstrating calibrated predictions and competitive or superior point estimates on challenging datasets.
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 in order 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.