This paper presents a novel camera relocalization method, STDLoc, which leverages Feature Gaussian as scene representation. STDLoc is a full relocalization pipeline that can achieve accurate relocalization without relying on any pose prior. Unlike previous coarse-to-fine localization methods that require image retrieval first and then feature matching, we propose a novel sparse-to-dense localization paradigm. Based on this scene representation, we introduce a novel matching-oriented Gaussian sampling strategy and a scene-specific detector to achieve efficient and robust initial pose estimation. Furthermore, based on the initial localization results, we align the query feature map to the Gaussian feature field by dense feature matching to enable accurate localization. The experiments on indoor and outdoor datasets show that STDLoc outperforms current state-of-the-art localization methods in terms of localization accuracy and recall. Our code is available on the project website: https://zju3dv.github.io/STDLoc.

2D-3D matching is an essential step for visual localization, where the accuracy of the camera pose is mainly determined by the quality of 2D-3D correspondences. The matching is typically achieved by the nearest neighbor search of local features. Many existing works have shown impressive results on both the efficiency and accuracy. Recently emerged learning-based features further improve the robustness compared to the traditional hand-crafted ones. However, it is still hard to establish enough correct matches in challenging scenes with illumination changes or repetitive patterns due to the intrinsic local properties of local features. In this work, we propose a novel method to deal with 2D-3D matching in a very robust way. We first establish as many potential correct matches as possible using the local similarity. Then we construct a bipartite graph and use a deep neural network, referred to as Bipartite Graph Network (BGNet), to extract the global geometric information. The network predicts the likelihood of being an inlier for each edge and outputs the globally optimal one-to-one correspondences with a Hungarian pooling layer. The experiments show that the proposed method can find more correct matches and improves localization on both the robustness and accuracy. The results on multiple visual localization datasets are obviously better than the existing state-of-the-arts, which demonstrates the effectiveness of the proposed method.