RESUMEN
The widespread use of deep learning in processing point cloud data promotes the development of neural networks designed for point clouds. Point-based methods are increasingly becoming the mainstream in point cloud neural networks due to their high efficiency and performance. However, most of these methods struggle to balance both the geometric and semantic space of the point cloud, which usually leads to unclear local feature aggregation in geometric space and poor global feature extraction in semantic space. To address these two defects, we propose a bilateral feature fusion module capable of combining geometric and semantic data from the point cloud to enhance local feature extraction. In addition, we propose an offset vector attention module for better extraction of global features from point clouds. We provide specific ablation studies and visualizations in the article to validate our key modules. Experimental results show that the proposed method performs superior in both point cloud classification and segmentation tasks.