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 this https URL for
a visual overview of our method.

使用单一 RGB 图像的非标定旋转估计算法 U-ARE-ME，利用曼哈顿世界假设和像素几何先验，在 SO (3) 流形上进行优化，实现对相机旋转及其不确定性的估计，并且通过多帧优化达到稳健性和时间一致性。与 RGB-D 方法相比，U-ARE-ME 性能相当；与稀疏特征为基础的 SLAM 方法相比，U-ARE-ME 更为稳健。可通过附带的视频链接获取我们方法的视觉概述。

U-ARE-ME：曼哈顿环境中的不确定性感知旋转估计

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

We present an approach to estimating camera rotation in crowded, real-world
scenes from handheld monocular video. While camera rotation estimation is a
well-studied problem, no previous methods exhibit both high accuracy and
acceptable speed in this setting. Because the setting is not addressed well by
other datasets, we provide a new dataset and benchmark, with high-accuracy,
rigorously verified ground truth, on 17 video sequences. Methods developed for
wide baseline stereo (e.g., 5-point methods) perform poorly on monocular video.
On the other hand, methods used in autonomous driving (e.g., SLAM) leverage
specific sensor setups, specific motion models, or local optimization
strategies (lagging batch processing) and do not generalize well to handheld
video. Finally, for dynamic scenes, commonly used robustification techniques
like RANSAC require large numbers of iterations, and become prohibitively slow.
We introduce a novel generalization of the Hough transform on SO(3) to
efficiently and robustly find the camera rotation most compatible with optical
flow. Among comparably fast methods, ours reduces error by almost 50\% over the
next best, and is more accurate than any method, irrespective of speed. This
represents a strong new performance point for crowded scenes, an important
setting for computer vision. The code and the dataset are available at
this https URL

我们提出了一种从手持单目视频中估计拥挤真实场景中相机旋转的方法，通过在 SO (3) 上引入 Hough 变换，可在光流中高效而鲁棒地找到与之最相符的相机旋转，且在速度上比其他方法更准确。