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.
Abstract (translated)
环境感知系统在高精度地图绘制和自主导航中扮演着至关重要的角色，而激光雷达（LiDAR）作为核心传感器提供了精确的三维点云数据。如何高效处理无结构化的点云并从中提取出有组织的语义信息仍然是一个重大挑战。近年来，许多基于伪图像表示的方法涌现出来，在效率与性能之间寻求平衡，然而这些方法往往忽视了点云中的结构性和语义细节，导致特征融合有限且分类能力不足。 在此研究中，我们提出了DAGLFNet框架，这是一个基于伪图像的语义分割框架，旨在提取具有区分性的特征。首先，使用全局-局部特征融合编码模块来增强集合内局部特征之间的相关性，并捕获全局上下文信息；其次，采用多分支特征提取网络来捕捉更多的邻域信息并加强轮廓特征的分类能力；最后，通过深度特征引导注意力机制引入跨通道特征融合以提高精度。 实验评估表明，在SemanticKITTI和nuScenes验证集上，DAGLFNet分别取得了69.83%和78.65%的成绩。该方法在保持高性能的同时兼顾实时处理的能力，显示出了基于LiDAR的实时应用的巨大潜力。
URL
https://arxiv.org/abs/2510.10471
