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LVCA-Net: Lightweight LiDAR Semantic Segmentation for Advanced Sensor-Based Perception in Autonomous Transportation
Yuxuan Gong1, Yuanhao Huang2,3, Li Bao1
1School of Aviation, Inner Mongolia University of Technology, Hohhot 010051, China.
LVCA-Net offers efficient 3D semantic segmentation for autonomous driving using LiDAR data. This lightweight framework achieves high accuracy and real-time performance for robust scene understanding.
Area of Science:
- Computer Vision
- Robotics
- Artificial Intelligence
Background:
- Autonomous systems require reliable 3D scene understanding from LiDAR.
- Sparse and irregular LiDAR point clouds present challenges for semantic interpretation.
Purpose of the Study:
- To propose LVCA-Net, a lightweight framework for efficient LiDAR-based 3D semantic segmentation.
- To enhance 3D scene understanding for autonomous driving applications.
Main Methods:
- Developed a lightweight voxel-coordinate attention framework (LVCA-Net).
- Integrated anisotropic depthwise residual module, LiteDown-LiteUp pathway, and Coordinate-Guided Sparse Semantic Module.
- Utilized a cylindrical voxel space for enhanced spatial consistency and sparsity.
Main Results:
- Achieved 67.17% mIoU and 91.79% accuracy on SemanticKITTI.
- Obtained 77.1% mIoU on nuScenes benchmark.
- Demonstrated real-time inference efficiency.
Conclusions:
- LVCA-Net provides scalable and robust 3D scene understanding for LiDAR-only perception.
- The framework is suitable for deployment in autonomous vehicles and safety-critical systems.
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