Although many notable improvements have been devoted to the semantic segmentation of laser scanning (LS) data, the extreme complexity of scanned scenes poses significant challenges in achieving the effective distribution of a category label per point. This study investigates the semantic segmentation of LiDAR point clouds using an improved deep learning method. In particular, the raw data were reorganized based on group proposals using Gaussian learning. We generated a structured multi-scale graph for group proposals, which supports multi-scale analysis in the scale space. Subsequently, a self-adaptive graph convolution network (GCN) was adopted to obtain the best point cloud features. Based on this GCN module, the proposals were semantically labeled by an encoder-decoder network. The proposed level inferences were finally transformed into point-wise predictions. For segmentation result refinement, the output probabilities of the proposed framework were weighted as the input of a developed conditional random field (CRF) algorithm. Experiments with three typical datasets (i.e., ParisLille-3D, Semantic3D, and vKITTI) comprehensively evaluated the performance of our approach. The experimental results demonstrated that the proposed framework can achieve better performance for several objects.

尽管许多显着的改进已经致力于激光扫描（LS）数据的语义分割，但扫描场景的极端复杂性对实现每个点的类别标签的有效分布提出了重大挑战。本研究使用改进的深度学习方法研究 LiDAR 点云的语义分割。特别是，原始数据是根据使用高斯学习的小组建议重新组织的。我们为组提案生成了一个结构化的多尺度图，它支持尺度空间中的多尺度分析。随后，采用自适应图卷积网络（GCN）来获得最佳点云特征。基于这个 GCN 模块，提案被编码器-解码器网络语义标记。提出的级别推断最终转化为逐点预测。对于分割结果的细化，所提出框架的输出概率被加权作为开发的条件随机场（CRF）算法的输入。使用三个典型数据集（即 ParisLille-3D、Semantic3D 和 vKITTI）进行的实验全面评估了我们方法的性能。实验结果表明，所提出的框架可以在多个对象上实现更好的性能。