Semantic segmentation of sensed point cloud data plays a significant role in scene understanding and reconstruction, robot navigation, etc. This paper presents a $k$NN-based 3D semantic segmentation network, which is a structural model for directly processing the unorganized point clouds. The network consists of three modules: point feature extraction, local feature extraction, and semantic segmentation. The first module is designed based on the simplified PointNet to extract powerful high-dimensional point features. Local feature extraction module, the key component of the proposed network, utilizes the $k$NN algorithm to search $k$-neighbors of each query point to extract the local and global features. Then the final semantic segmentation part concatenates the extracted features to learn and label the input point clouds. Experimental results on the indoor and outdoor datasets show that the proposed work settles the shortcoming of insufficient local feature extraction of existing models and promotes the accuracy of semantic segmentation.

感知点云数据的语义分割在场景理解和重建、机器人导航等方面发挥着重要作用。 $克$基于NN的3D语义分割网络，是一种直接处理无组织点云的结构模型。该网络由三个模块组成：点特征提取、局部特征提取和语义分割。第一个模块是基于简化的PointNet设计的，用于提取强大的高维点特征。局部特征提取模块，所提出的网络的关键组成部分，利用$克$NN算法进行搜索 $克$- 每个查询点的邻居以提取局部和全局特征。然后最后的语义分割部分连接提取的特征来学习和标记输入点云。在室内和室外数据集上的实验结果表明，所提出的工作解决了现有模型局部特征提取不足的缺点，提高了语义分割的准确性。