  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 costs 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 complex 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 simple
outdoor dataset to evaluate the capabilities of visual localization methods in
scene-specific 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:
https://thpjp.github.io/d2s.
