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GENet: A Geometry-Enhanced Network for LiDAR Semantic Segmentation.

Yuchen Wu¹, Hanbing Wei¹

¹School of Mechatronics and Vehicle Engineering, Chongqing Jiaotong University, Chongqing 400074, China.

Sensors (Basel, Switzerland)

|March 14, 2026

Summary

This summary is machine-generated.

This study introduces GENet, a novel LiDAR segmentation network that enhances accuracy by leveraging geometric information. GENet achieves real-time performance with fewer parameters and less computation than existing methods.

Keywords:

LiDAR point cloud feature fusion geometry attention range view semantic segmentation

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Area of Science:

Computer Vision
Robotics
LiDAR Technology

Background:

LiDAR is crucial for autonomous driving and robotics.
Real-time point cloud segmentation is vital for accuracy and speed.
Existing 2D projection methods lose spatial information.

Purpose of the Study:

To develop a real-time LiDAR segmentation method that overcomes spatial information loss.
To propose a network that effectively utilizes spatial priors for improved segmentation.
To achieve a balance between accuracy and computational efficiency.

Main Methods:

Introduced GENet (Geometry-Enhanced Network) utilizing spatial priors.
Employed Atrous Separable Range Attention (ASRA) for geometry-aware feature aggregation.
Utilized Geometry-Context Modulation (GCM) to calibrate semantic features with geometric priors.

Main Results:

GENet achieves efficient information fusion and real-time performance.
The method requires fewer parameters and less computation compared to existing approaches.
Demonstrated a favorable balance between segmentation accuracy and computational efficiency.

Conclusions:

GENet effectively exploits spatial priors for enhanced LiDAR point cloud segmentation.
The proposed ASRA and GCM modules contribute to geometry-aware feature aggregation and calibration.
GENet offers a promising solution for real-time LiDAR segmentation in autonomous systems.