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3D Geometry-Aware Efficient Feature Matching for Weakly Textured Scenes.

Libo Sun¹, Yidong Yan¹, Wenqi Yang¹

¹School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China.

Journal of Imaging

|June 25, 2026

Summary

This summary is machine-generated.

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This study introduces Geometry-Aware Efficient Feature (GAEFeat), a lightweight network for robust local feature matching in industrial settings. GAEFeat excels in weakly textured environments, offering efficient and accurate visual localization.

Area of Science:

Robotics
Computer Vision
Machine Learning

Background:

Local feature matching is crucial for robotic SLAM and visual localization.
Weakly textured industrial environments pose challenges for traditional appearance-based methods.
Existing lightweight methods struggle with discriminative and stable feature learning in these settings.

Purpose of the Study:

To propose GAEFeat, a novel lightweight vision-geometric feature learning network.
To address the lack of specialized training data for feature learning in industrial environments.
To enhance the robustness and efficiency of local feature matching in challenging scenes.

Main Methods:

Developed a lightweight vision-geometric feature learning network named GAEFeat.

Keywords:

RGB-D geometry edge computing feature matching industrial vision kinematics supervision weak texture

Related Experiment Videos

Integrated robotic arm pose priors and depth information for automated cross-view supervision and surface-normal labels.

Designed a dual enhancement mechanism with a geometric auxiliary branch and a geometry-aware enhancement (GAE) module.

Constructed simulated and real-world datasets for training and evaluation.

Main Results:

GAEFeat demonstrates strong robustness and high inference efficiency in relative pose estimation, homography estimation, and visual localization.
Achieved notable advantages in near-field, weakly textured industrial scenes.
Inference latency of 3.9 ms on NVIDIA Jetson AGX Orin, indicating real-time capability.

Conclusions:

GAEFeat effectively learns discriminative and stable local features for weakly textured industrial environments.
The proposed method offers practical potential for deployment in edge computing environments.
The integration of geometric priors significantly improves feature matching performance and efficiency.