We present CPO, a fast and robust algorithm that localizes a 2D panorama with respect to a 3D point cloud of a scene possibly containing changes. To robustly handle scene changes, our approach deviates from conventional feature point matching, and focuses on the spatial context provided from panorama images. Specifically, we propose efficient color histogram generation and subsequent robust localization using score maps. By utilizing the unique equivariance of spherical projections, we propose very fast color histogram generation for a large number of camera poses without explicitly rendering images for all candidate poses. We accumulate the regional consistency of the panorama and point cloud as 2D/3D score maps, and use them to weigh the input color values to further increase robustness. The weighted color distribution quickly finds good initial poses and achieves stable convergence for gradient-based optimization. CPO is lightweight and achieves effective localization in all tested scenarios, showing stable performance despite scene changes, repetitive structures, or featureless regions, which are typical challenges for visual localization with perspective cameras. Code is available at \url{https://github.com/82magnolia/panoramic-localization/}.

翻译：我们提出CPO，一种快速鲁棒的算法，能够将二维全景图与可能包含场景变化的三维点云进行定位。为鲁棒处理场景变化，本方法摒弃传统特征点匹配，转而利用全景图像提供的空间上下文。具体而言，我们提出高效的色彩直方图生成方法，并基于得分图实现鲁棒定位。借助球面投影的独特等变性，我们无需为所有候选位姿显式渲染图像，即可快速生成大量相机位姿的色彩直方图。将全景图与点云的区域一致性累积为2D/3D得分图，并通过其加权输入色彩值以进一步提升鲁棒性。加权色彩分布能够快速找到优质初始位姿，并实现梯度优化下的稳定收敛。CPO轻量化且在所有测试场景中均实现有效定位，在场景变化、重复结构或无纹理区域等透视相机视觉定位典型挑战下仍保持稳定性能。代码已开源在\url{https://github.com/82magnolia/panoramic-localization/}。