  This study addresses the challenge of performing visual localization in
demanding conditions such as night-time scenarios, adverse weather, and
seasonal changes. While many prior studies have focused on improving
image-matching performance to facilitate reliable dense keypoint matching
between images, existing methods often heavily rely on predefined feature
points on a reconstructed 3D model. Consequently, they tend to overlook
unobserved keypoints during the matching process. Therefore, dense keypoint
matches are not fully exploited, leading to a notable reduction in accuracy,
particularly in noisy scenes. To tackle this issue, we propose a novel
localization method that extracts reliable semi-dense 2D-3D matching points
based on dense keypoint matches. This approach involves regressing semi-dense
2D keypoints into 3D scene coordinates using a point inference network. The
network utilizes both geometric and visual cues to effectively infer 3D
coordinates for unobserved keypoints from the observed ones. The abundance of
matching information significantly enhances the accuracy of camera pose
estimation, even in scenarios involving noisy or sparse 3D models.
Comprehensive evaluations demonstrate that the proposed method outperforms
other methods in challenging scenes and achieves competitive results in
large-scale visual localization benchmarks. The code will be available.
