State-of-the-art visual localization methods mostly rely on complex
procedures to match local descriptors and 3D point clouds. However, these
procedures can incur significant cost in terms of inference, storage, and
updates over time. In this study, we propose a direct learning-based approach
that utilizes a simple network named D2S to represent local descriptors and
their scene coordinates. Our method is characterized by its simplicity and
cost-effectiveness. It solely leverages a single RGB image for localization
during the testing phase and only requires a lightweight model to encode a
complex sparse scene. The proposed D2S employs a combination of a simple loss
function and graph attention to selectively focus on robust descriptors while
disregarding areas such as clouds, trees, and several dynamic objects. This
selective attention enables D2S to effectively perform a binary-semantic
classification for sparse descriptors. Additionally, we propose a new outdoor
dataset to evaluate the capabilities of visual localization methods in terms of
scene generalization and self-updating from unlabeled observations. Our
approach outperforms the state-of-the-art CNN-based methods in scene coordinate
regression in indoor and outdoor environments. It demonstrates the ability to
generalize beyond training data, including scenarios involving transitions from
day to night and adapting to domain shifts, even in the absence of the labeled
data sources. The source code, trained models, dataset, and demo videos are
available at the following link: this https URL

本研究提出了一种基于学习的直接定位方法，使用简单的网络 D2S 来表示本地描述符和场景坐标，通过选择性关注鲁棒描述符并忽略云、树木和其他动态物体的方法，完成了稀疏描述符的二元 - 语义分类，在室内和室外环境中超越了现有的基于 CNN 的方法。