U–ARE–ME: Uncertainty-Aware Rotation Estimation in Manhattan Environments

Aalok Patwardhan, Callum Rhodes, Gwangbin Bae, Andrew Davison

Keywords: Rotation Estimation, Manhattan World

TL;DR: Camera rotation estimator for uncalibrated images and video with rotation uncertainty and temporal consistency

Abstract:

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 feature-based vanishing point and SLAM methods.

Submission Number: 292