A large-scale point cloud semantic segmentation network via local dual features and global correlations

Abstract

For large-scale point cloud semantic segmentation, the relationships between long-range neighbourhoods are as important as short-range features. The current methods focus on aggregating the fine-grained geometric structures of local regions, the long-range features are usually ignored. To this end, we propose a novel framework named LG-Net that can efficiently learn local dual features and global correlations from point clouds. We adopt random sampling to decrease point density, thereby processing large-scale point cloud efficiently. Specifically, we propose a dual feature complementary (DFC) module that makes geometric and semantic features complementary to preserve local information. We utilize positional relations to capture geometric details, and aggregate semantic features according to the similarity of neighbours. The global correlation mining (GCM) module simply shares an attention map for all 3D points to learn long-range contextual information. We conducted extensive experiments on public datasets, including indoor scenes (S3DIS, ScanNet) and outdoor scenes (SemanticKITTI, SensatUrban). Experimental results demonstrate that our method outperforms the state-of-the-art approaches.