Re-localizing a camera from a single image in a previously mapped area is
vital for many computer vision applications in robotics and augmented/virtual
reality. In this work, we address the problem of estimating the 6 DoF camera
pose relative to a global frame from a single image. We propose to leverage a
novel network of relative spatial and temporal geometric constraints to guide
the training of a Deep Network for localization. We employ simultaneously
spatial and temporal relative pose constraints that are obtained not only from
adjacent camera frames but also from camera frames that are distant in the
spatio-temporal space of the scene. We show that our method, through these
constraints, is capable of learning to localize when little or very sparse
ground-truth 3D coordinates are available. In our experiments, this is less
than 1% of available ground-truth data. We evaluate our method on 3 common
visual localization datasets and show that it outperforms other direct pose
estimation methods.

从单个图像中估计相对于全局框架的六自由度相机姿态的问题，我们提出了一种利用相对空间和时间几何约束的新颖网络来引导定位的深度网络的训练方法。利用相邻相机帧和场景中时空距离较远的相机帧获得的空间和时间相对姿态约束，我们的方法能够学习到在只有少量或非常稀疏的地面真实 3D 坐标可用的情况下进行定位。我们在三个常见的视觉定位数据集上评估了我们的方法，并证明其优于其他直接姿态估计方法。