一种车载LiDAR点云道路提取深度神经网络模型

A deep neural network model for road extraction of MLS LiDAR point cloud

PointNet++在车载LiDAR点云道路提取中表现出优于传统方法的性能,但对于道路边缘的提取仍存在过分割或欠分割的现象。针对该问题,本文提出了一种改进的邻域增强编码网络——E-PointNet++,通过在特征提取前引入一个邻域增强编码模块,建立局部邻域内点与点之间的联系,以提高网络的道路边缘分割能力。在两个数据集上进行对比试验,E-PointNet++表现出明显优于其他方法的性能,准确性、完整性和检测质量均高于97%。该方法对于不同数据集和场景表现稳健。

PointNet++has shown better performance than traditional methods in MLS LiDAR point cloud road extraction,but there are still the phenomena of over segmentation or under segmentation for road edge extraction.To address this issue,an improved neighborhood enhancement coding network E-PointNet++is proposed.By introducing a neighborhood enhancement coding module before feature extraction,the connection between local neighborhood points is established to improve the network's road edge segmentation ability.Comparative experiments are conducted on two datasets,and E-PointNet++shows significantly better performance than other methods,with accuracy,integrity and detection quality all exceeding 97%.This method performs robustly on different datasets and scenarios.