Global visual localization estimates the absolute pose of a camera using a
single image, in a previously mapped area. Obtaining the pose from a single
image enables many robotics and augmented/virtual reality applications.
Inspired by latest advances in deep learning, many existing approaches directly
learn and regress 6 DoF pose from an input image. However, these methods do not
fully utilize the underlying scene geometry for pose regression. The challenge
in monocular relocalization is the minimal availability of supervised training
data, which is just the corresponding 6 DoF poses of the images. In this paper,
we propose to utilize these minimal available labels (.i.e, poses) to learn the
underlying 3D geometry of the scene and use the geometry to estimate the 6 DoF
camera pose. We present a learning method that uses these pose labels and rigid
alignment to learn two 3D geometric representations (\textit{X, Y, Z
coordinates}) of the scene, one in camera coordinate frame and the other in
global coordinate frame. Given a single image, it estimates these two 3D scene
representations, which are then aligned to estimate a pose that matches the
pose label. This formulation allows for the active inclusion of additional
learning constraints to minimize 3D alignment errors between the two 3D scene
representations, and 2D re-projection errors between the 3D global scene
representation and 2D image pixels, resulting in improved localization
accuracy. During inference, our model estimates the 3D scene geometry in camera
and global frames and aligns them rigidly to obtain pose in real-time. We
evaluate our work on three common visual localization datasets, conduct
ablation studies, and show that our method exceeds state-of-the-art regression
methods' pose accuracy on all datasets.

通过利用少量的标签数据（即相机姿态），本文提出了一种学习方法，将这些标签与刚性对齐相结合，学习场景的三维几何表示，并将其用于估计相机的六自由度姿态，从而提高了定位的准确性。

从姿态中隐式学习场景几何用于全局定位

Implicit Learning of Scene Geometry from Poses for Global Localization

We present a robust and real-time monocular six degree of freedom
relocalization system. Our system trains a convolutional neural network to
regress the 6-DOF camera pose from a single RGB image in an end-to-end manner
with no need of additional engineering or graph optimisation. The algorithm can
operate indoors and outdoors in real time, taking 5ms per frame to compute. It
obtains approximately 2m and 6 degree accuracy for large scale outdoor scenes
and 0.5m and 10 degree accuracy indoors. This is achieved using an efficient 23
layer deep convnet, demonstrating that convnets can be used to solve
complicated out of image plane regression problems. This was made possible by
leveraging transfer learning from large scale classification data. We show the
convnet localizes from high level features and is robust to difficult lighting,
motion blur and different camera intrinsics where point based SIFT registration
fails. Furthermore we show how the pose feature that is produced generalizes to
other scenes allowing us to regress pose with only a few dozen training
examples. PoseNet code, dataset and an online demonstration is available on our
project webpage, at this http URL

本文提出了一个稳健而实时的单目六自由度重定位系统，使用卷积神经网络训练完成，无需其他工程或优化过程。