NLA-GCL-Net: semantic segmentation of large-scale surveying point clouds based on neighborhood label aggregation (NLA) and global context learning (GCL)

For large-scale 3D point clouds from surveying, the RandLA-Net semantic segmentation network model is unable to learn global context features efficiently during training, leading to suboptimal feature acquisition of globally related features. Furthermore, inconsistent predictions are made in the vicinity of data boundaries due to the oversimplified usage of multi-layer perceptrons (MLPs) and non-linear activation functions following decoding, which has a detrimental impact on both model training and performance. This study provides two solutions to address these issues: a neighborhood label aggregation (NLA) classifier that aggregates neighborhood information for segmentation tasks, and a global context learning (GCL) module that learns global volume-relative information to improve the semantic segmentation network. Overall accuracy (OA) obtained via experimental validation on large-scale SensatUrban and S3DIS datasets is 91.80% and 88.8%, respectively. The proposed model outperforms RandLA-Net by 3.8% and 3.0% in overall mean Intersection over Union (mIOU), respectively, significantly improving model performance and generalization abilities. This work offers new perspectives on how to effectively segment point cloud data.