  Camera localization is a classical computer vision task that serves various
Artificial Intelligence and Robotics applications. With the rapid developments
of Deep Neural Networks (DNNs), end-to-end visual localization methods are
prosperous in recent years. In this work, we focus on the scene coordinate
prediction ones and propose a network architecture named as Structure Guidance
Learning (SGL) which utilizes the receptive branch and the structure branch to
extract both high-level and low-level features to estimate the 3D coordinates.
We design a confidence strategy to refine and filter the predicted 3D
observations, which enables us to estimate the camera poses by employing the
Perspective-n-Point (PnP) with RANSAC. In the training part, we design the
Bundle Adjustment trainer to help the network fit the scenes better.
Comparisons with some state-of-the-art (SOTA) methods and sufficient ablation
experiments confirm the validity of our proposed architecture.
