Learning-based visual relocalizers exhibit leading pose accuracy, but require
hours or days of training. Since training needs to happen on each new scene
again, long training times make learning-based relocalization impractical for
most applications, despite its promise of high accuracy. In this paper we show
how such a system can actually achieve the same accuracy in less than 5
minutes. We start from the obvious: a relocalization network can be split in a
scene-agnostic feature backbone, and a scene-specific prediction head. Less
obvious: using an MLP prediction head allows us to optimize across thousands of
view points simultaneously in each single training iteration. This leads to
stable and extremely fast convergence. Furthermore, we substitute effective but
slow end-to-end training using a robust pose solver with a curriculum over a
reprojection loss. Our approach does not require privileged knowledge, such a
depth maps or a 3D model, for speedy training. Overall, our approach is up to
300x faster in mapping than state-of-the-art scene coordinate regression, while
keeping accuracy on par.

本文介绍了一种学习基础的视觉重定位系统，利用无场景特异性特征骨干和场景特异性预测模型，结合多点并行迭代训练来提高定位精度，并使用反投影损失替代复杂的末端到末端训练过程，从而提高训练速度，而且优于最先进的场景坐标回归方法。