  Estimating the 3DoF rotation from a single RGB image is an important yet
challenging problem. Probabilistic rotation regression has raised more and more
attention with the benefit of expressing uncertainty information along with the
prediction. Though modeling noise using Gaussian-resembling Bingham
distribution and matrix Fisher distribution is natural, they are shown to be
sensitive to outliers for the nature of quadratic punishment to deviations. In
this paper, we draw inspiration from multivariate Laplace distribution and
propose a novel Rotation Laplace distribution on SO(3). Rotation Laplace
distribution is robust to the disturbance of outliers and enforces much
gradient to the low-error region, resulting in a better convergence. Our
extensive experiments show that our proposed distribution achieves
state-of-the-art performance for rotation regression tasks over both
probabilistic and non-probabilistic baselines. Our project page is at
https://pku-epic.github.io/RotationLaplace.
