基于多模态投影的激光雷达点云实时语义分割网络

LiDAR Semantic Segmentation Network for Real-Time Multimodal Projection

针对激光雷达点云语义分割过程中,传统方法无法权衡检测速度和精度的问题,提出了一种多模态融合的激光雷达点云语义分割网络架构。利用点-网格模块提取语义特征,经过空间注意力模块聚合空间和上下文信息,通过二维全卷积网络(FCN)特征融合金字塔模块实现语义分割,最后通过二维和三维特征融合减少信息损失,并使用损失函数更新权重优化模型。SemanticKITTI数据集上的验证结果表明,该模型平均交并比达到63.3%,与其他优秀算法相比兼顾了实时性与准确性,显著提高了激光雷达点云语义分割的精度。

Traditional methods based on points cannot balance the detection speed and accuracy in LiDAR semantic segmentation.To address this issue,this paper proposes a multimodal fusion LiDAR semantic segmentation network.Semantic features are extracted through the point-grid module,spatial and contextual information is aggregated through the attention mechanism module,semantic segmentation is achieved through the 2D Fully Convolutional Network(FCN)feature fusion pyramid,and finally,information loss is reduced through the fusion of 2D and 3D features,and the weights are updated to optimize the model using the loss function.Verification of SemanticKITTI dataset indicates that this model achieves an average crossover ratio of 63.3%,and takes into account of real-time property and accuracy as compared with other algorithms,which significantly improves the accuracy of LiDAR semantic segmentation.