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

arXiv:2403.15583v1 Announce Type: new
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 …

arxiv cs.cv environments rotation type uncertainty