Visual localization, i.e., the problem of camera pose estimation, is a
central component of applications such as autonomous robots and augmented
reality systems. A dominant approach in the literature, shown to scale to large
scenes and to handle complex illumination and seasonal changes, is based on
local features extracted from images. The scene representation is a sparse
Structure-from-Motion point cloud that is tied to a specific local feature.
Switching to another feature type requires an expensive feature matching step
between the database images used to construct the point cloud. In this work, we
thus explore a more flexible alternative based on dense 3D meshes that does not
require features matching between database images to build the scene
representation. We show that this approach can achieve state-of-the-art
results. We further show that surprisingly competitive results can be obtained
when extracting features on renderings of these meshes, without any neural
rendering stage, and even when rendering raw scene geometry without color or
texture. Our results show that dense 3D model-based representations are a
promising alternative to existing representations and point to interesting and
challenging directions for future research.

通过探索一种基于密集三维网格的更为灵活的方法，本文旨在解决基于图像的特征匹配对在构建场景表示上造成的代价，并通过在渲染场景几何图形时提取特征的方式，展示了这一方法达到了最新成果。结果表明，基于密集三维模型的表示是现有表示的一种有前途的替代方案，并且为未来研究提出了有趣而具有挑战性的方向。