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.

CPO 是一种针对 2D 全景图像和 3D 点云进行快速和鲁棒定位的算法，通过使用色彩直方图和得分图进行可靠定位，其重点是处理包含场景变化的情况。