【摘要】 目前点云语义分割已广泛应用到自动驾驶、虚拟现实等多个领域，但现阶段点云分割算法无法提取较完整的空间结构信息，难以解释每个点编码信息的问题.针对此缺陷，文中提出基于编码特征学习的3D点云语义分割网络.首先，在引入角度信息和增强特征的基础上构造局部特征编码器(Local Feature Encoder, LFE),学习较完整的局部空间结构，缓解相似物体错分割问题.然后，设计混合池化聚合模块(Mixed Pooling Polymerization, MPP),聚合粗犷特征和精细特征，同时保证点云的排序不变性.最后，采用多尺度特征融合，充分利用编码层不同尺度特征，实现准确的语义分割.在两个大型基准数据集S3DIS和SemanticKITTI上的实验表明文中网络的优越性.更多还原

【Abstract】 Now point cloud semantic segmentation is widely applied in various fields such as autonomous driving and virtual reality. However, the current point cloud semantic segmentation algorithms cannot extract relatively complete spatial structure information, and the information for each point is difficult to explain. To address this deficiency, a 3D point cloud semantic segmentation network based on coding feature learning is proposed. Firstly, the local feature encoder is designed based on the introduction of angle information and the enhanced features to learn more complete local spatial structures and alleviate the problem of misclassification of similar objects. Secondly, mixed pooling polymerization module is designed to aggregate rough features and fine features while ensuring the sorting invariance of point cloud. Finally, the multi-scale feature fusion is adopted to fully utilize the different scale features in the encoding layer and achieve accurate semantic segmentation. The experiment on two large benchmark datasets, S3DIS and SemanticKITTI, demonstrates the superiority of the proposed network.更多还原