title: LiftFeat: 3D Geometry-Aware Local Feature Matching
summary: 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.
paper_authors._id: 681db58b1f1c39ba8fbe0162
paper_authors.name: Yepeng Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-09T08:33:21.362Z
paper_authors._id: 681db120007a2d4056d25c70
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/XyX4OSVmH0zv9qQjFSMGd.png
paper_authors.fullname: yepeng liu
paper_authors.type: user
paper_authors.user: pengliu123
paper_authors._id: 681db58b1f1c39ba8fbe0163
paper_authors.name: Wenpeng Lai
paper_authors._id: 681db58b1f1c39ba8fbe0164
paper_authors.name: Zhou Zhao
paper_authors._id: 681db58b1f1c39ba8fbe0165
paper_authors.name: Yuxuan Xiong
paper_authors._id: 681db58b1f1c39ba8fbe0166
paper_authors.name: Jinchi Zhu
paper_authors._id: 681db58b1f1c39ba8fbe0167
paper_authors.name: Jun Cheng
paper_authors._id: 681db58b1f1c39ba8fbe0168
paper_authors.name: Yongchao Xu
paper_title: LiftFeat: 3D Geometry-Aware Local Feature Matching
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: monocular depth estimation pseudo surface normal 3D geometry-aware feature lifting relative pose estimation homography estimation visual localization