Robust and efficient local feature matching plays a crucial role in
applications such as SLAM and visual localization for robotics. Despite great
progress, it is still very challenging to extract robust and discriminative
visual features in scenarios with drastic lighting changes, low texture areas,
or repetitive patterns. In this paper, we propose a new lightweight network
called LiftFeat, which lifts the robustness of raw descriptor by
aggregating 3D geometric feature. Specifically, we first adopt a pre-trained
monocular depth estimation model to generate pseudo surface normal label,
supervising the extraction of 3D geometric feature in terms of predicted
surface normal. We then design a 3D geometry-aware feature lifting module to
fuse surface normal feature with raw 2D descriptor feature. Integrating such 3D
geometric feature enhances the discriminative ability of 2D feature description
in extreme conditions. Extensive experimental results on relative pose
estimation, homography estimation, and visual localization tasks, demonstrate
that our LiftFeat outperforms some lightweight state-of-the-art methods. Code
will be released at : https://github.com/lyp-deeplearning/LiftFeat.