DAGLFNet:Deep Attention-Guided Global-Local Feature Fusion for Pseudo-Image Point Cloud Segmentation

• URL: http://arxiv.org/abs/2510.10471v1
• Date: Sun, 12 Oct 2025 06:35:03 GMT
• Title: DAGLFNet:Deep Attention-Guided Global-Local Feature Fusion for Pseudo-Image Point Cloud Segmentation
• Authors: Chuang Chen, Wenyi Ge,
• Abstract summary: We propose DAGLFNet, a pseudo-image-based representation method to extract discriminative features from point clouds.<n>The method balances high performance with real-time capability, demonstrating great potential for LiDAR-based real-time applications.
• Score: 6.418552842518015
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Environmental perception systems play a critical role in high-precision mapping and autonomous navigation, with LiDAR serving as a core sensor that provides accurate 3D point cloud data. How to efficiently process unstructured point clouds while extracting structured semantic information remains a significant challenge, and in recent years, numerous pseudo-image-based representation methods have emerged to achieve a balance between efficiency and performance. However, they often overlook the structural and semantic details of point clouds, resulting in limited feature fusion and discriminability. In this work, we propose DAGLFNet, a pseudo-image-based semantic segmentation framework designed to extract discriminative features. First, the Global-Local Feature Fusion Encoding module is used to enhance the correlation among local features within a set and capture global contextual information. Second, the Multi-Branch Feature Extraction network is employed to capture more neighborhood information and enhance the discriminability of contour features. Finally, a Feature Fusion via Deep Feature-guided Attention mechanism is introduced to improve the precision of cross-channel feature fusion. Experimental evaluations show that DAGLFNet achieves 69.83\% and 78.65\% on the validation sets of SemanticKITTI and nuScenes, respectively. The method balances high performance with real-time capability, demonstrating great potential for LiDAR-based real-time applications.

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