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.

在夜间场景、恶劣天气和季节变化等苛刻条件下进行视觉定位是一项具有挑战性的任务。本研究提出了一种新颖的定位方法，通过提取可靠的半稠密的 2D-3D 匹配点来改进相机位姿估计的准确性，即使在噪声场景中也能取得显著的成果。

学习产生半密集对应关系进行视觉定位

Learning to Produce Semi-dense Correspondences for Visual Localization

In this paper, we present a method to utilize 2D-2D point matches between
images taken during different image conditions to train a convolutional neural
network for semantic segmentation. Enforcing label consistency across the
matches makes the final segmentation algorithm robust to seasonal changes. We
describe how these 2D-2D matches can be generated with little human interaction
by geometrically matching points from 3D models built from images. Two
cross-season correspondence datasets are created providing 2D-2D matches across
seasonal changes as well as from day to night. The datasets are made publicly
available to facilitate further research. We show that adding the
correspondences as extra supervision during training improves the segmentation
performance of the convolutional neural network, making it more robust to
seasonal changes and weather conditions.

描述如何通过对 3D 模型中的点进行几何匹配从而生成 2D-2D 匹配，进而创建了两个跨季节和跨昼夜的匹配数据集，并在训练中添加这些匹配作为额外的监督来提高卷积神经网络的语义分割性能，使其更适应季节变化和天气条件。