Paper ID: 2304.05571

SGL: Structure Guidance Learning for Camera Localization

Xudong Zhang, Shuang Gao, Xiaohu Nan, Haikuan Ning, Yuchen Yang, Yishan Ping, Jixiang Wan, Shuzhou Dong, Jijunnan Li, Yandong Guo

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.

Submitted: Apr 12, 2023