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.
@article{arxiv.2304.05571,
title = {SGL: Structure Guidance Learning for Camera Localization},
author = {Xudong Zhang and Shuang Gao and Xiaohu Nan and Haikuan Ning and Yuchen Yang and Yishan Ping and Jixiang Wan and Shuzhou Dong and Jijunnan Li and Yandong Guo},
journal= {arXiv preprint arXiv:2304.05571},
year = {2023}
}