U-ARE-ME: Uncertainty-Aware Rotation Estimation in Manhattan
  Environments
Aalok Patwardhan Callum Rhodes Gwangbin Bae Andrew J. Davison
Camera rotation estimation from a single image is a challenging task, often
requiring depth data and/or camera intrinsics, which are generally not
available for in-the-wild videos. Although external sensors such as inertial
measurement units (IMUs) can help, they often suffer from drift and are not
applicable in non-inertial reference frames. We present U-ARE-ME, an algorithm
that estimates camera rotation along with uncertainty from uncalibrated RGB
images. Using a Manhattan World assumption, our method leverages the per-pixel
geometric priors encoded in single-image surface normal predictions and
performs optimisation over the SO(3) manifold. Given a sequence of images, we
can use the per-frame rotation estimates and their uncertainty to perform
multi-frame optimisation, achieving robustness and temporal consistency. Our
experiments demonstrate that U-ARE-ME performs comparably to RGB-D methods and
is more robust than sparse feature-based SLAM methods. We encourage the reader
to view the accompanying video at https://callum-rhodes.github.io/U-ARE-ME for
a visual overview of our method.