SGL: Structure Guidance Learning for Camera Localization

• URL: http://arxiv.org/abs/2304.05571v1
• Date: Wed, 12 Apr 2023 02:20:29 GMT
• Title: SGL: Structure Guidance Learning for Camera Localization
• Authors: Xudong Zhang, Shuang Gao, Xiaohu Nan, Haikuan Ning, Yuchen Yang, Yishan Ping, Jixiang Wan, Shuzhou Dong, Jijunnan Li, Yandong Guo
• Abstract summary: We propose a network architecture named Structure Guidance Bundle (SGL) which utilizes the receptive branch and the structure branch to extract both high-level and low-level features. In this work, we focus on the scene prediction ones and propose a network architecture named SGL which utilizes the receptive branch and the structure branch to extract both high-level and low-level features.
• Score: 7.094881396940598
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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