Point Cloud Semantic Segmentation Based on Boundary Awareness and Multiscale Spatial Descriptors (opens in new tab)

The 3-D point cloud semantic segmentation is essential for 3-D environmental perception and scene understanding. A key challenge lies in enhancing object boundary segmentation accuracy and capturing global contextual features efficiently. However, most existing methods optimize boundaries only through local feature enhancement or explicit boundary optimization, and struggle to comprehensively capture global information. Therefore, we propose BAMS-Net, a novel point cloud semantic segmentation...