Accurate estimates of rotation are crucial to vision-based motion estimation
in augmented reality and robotics. In this work, we present a method to extract
probabilistic estimates of rotation from deep regression models. First, we
build on prior work and argue that a multi-headed network structure we name
HydraNet provides better calibrated uncertainty estimates than methods that
rely on stochastic forward passes. Second, we extend HydraNet to targets that
belong to the rotation group, SO(3), by regressing unit quaternions and using
the tools of rotation averaging and uncertainty injection onto the manifold to
produce three-dimensional covariances. Finally, we present results and analysis
on a synthetic dataset, learn consistent orientation estimates on the 7-Scenes
dataset, and show how we can use our learned covariances to fuse deep estimates
of relative orientation with classical stereo visual odometry to improve
localization on the KITTI dataset.

该论文提出了一种从深度回归模型中提取旋转概率估计的方法，并将其扩展到 SO (3) 旋转群的目标上，该方法在合成和真实数据中都获得了良好的结果。