FAR-Net: Semantic Segmentation of Large-Scale Point Clouds Based on Feature Aggregation and Recoding for Aerial Computing

Semantic segmentation of large-scale point clouds is a research hotspot in aerospace remote sensing technology. Large-scale point clouds face the curse of dimensionality, and there are two problems to resolve, i.e., severe feature loss caused by down-sampling and tricky balance between segmentation performance and computational cost. In this paper, we propose the FAR-Net, a novel point cloud segmentation method based on feature aggregation-recoding, to infer point-level semantics of large-scale point clouds at reasonable cost. This network has three major features. First, local difference attention coding preserves feature details. Second, a hierarchical feature aggregation module based on keypoints and voxels is designed with two branches. The keypoint branch uses k-means clustering to generate keypoints that are globally associated by Point Transformer. The voxel branch adopts a multi-scale structure with three cascaded 3D CNN modules to extract local spatial features. Third, a binary feature recoding module divides intra-cluster point clouds into two categories by a binary classifier, and assigns keypoint features and difference features according to the binary label to increase feature differences. Experiments show that our network achieves competitive segmentation performance on both indoor and outdoor large-scale 3D point cloud datasets.