U-ARE-ME: Uncertainty-Aware Rotation Estimation in Manhattan Environments
This addresses the problem of camera rotation estimation for in-the-wild videos where depth data or sensors are unavailable, though it is incremental as it builds on existing Manhattan World assumptions.
The paper tackles camera rotation estimation from uncalibrated RGB images by proposing U-ARE-ME, which uses Manhattan World assumptions and surface normal predictions to achieve performance comparable to RGB-D methods and greater robustness than sparse feature-based SLAM.
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.