LiftFeat: 3D Geometry-Aware Local Feature Matching
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.
681db58b1f1c39ba8fbe0162
Yepeng Liu
claimed_verified
2025-05-09T08:33:21.362Z
681db120007a2d4056d25c70
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/XyX4OSVmH0zv9qQjFSMGd.png
yepeng liu
user
pengliu123
681db58b1f1c39ba8fbe0163
Wenpeng Lai
681db58b1f1c39ba8fbe0164
Zhou Zhao
681db58b1f1c39ba8fbe0165
Yuxuan Xiong
681db58b1f1c39ba8fbe0166
Jinchi Zhu
681db58b1f1c39ba8fbe0167
Jun Cheng
681db58b1f1c39ba8fbe0168
Yongchao Xu
LiftFeat: 3D Geometry-Aware Local Feature Matching
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.
LiftFeat, a lightweight network, improves local feature matching by integrating 3D geometric features with traditional 2D descriptors, enhancing robustness and discriminative ability in challenging conditions.
monocular depth estimation pseudo surface normal 3D geometry-aware feature lifting relative pose estimation homography estimation visual localization