关键词: 三维点云, 语义分割, 宽度学习系统, 特征融合, 注意力机制

Abstract: Semantic segmentation of three-dimensional point clouds is a key technology in the fields of autonomous driving and computer vision. Deep learning-based semantic segmentation methods suffer from inefficient operation due to their complex network structures. To address the inefficiency of deep neural networks in 3D point cloud semantic segmentation, Point-MLPBLS :Point cloud semantic segmentation network based on MLP cascaded broad learning system was proposed. First, The overall network was designed to adopt a dual network collaborative architecture: a feature extraction network and a point cloud segmentation network. Second, the feature extraction network was applied with sampling-grouping-aggregation operation to the input point cloud data and an inverted residual module was added to enhance local feature representation. Third, a feature propagation module was utilized to achieve upsampling, restores the original point cloud resolution, and performs multi-scale feature fusion to construct a refined point cloud representation. Finally, the point cloud segmentation network deep iterative training was replaced with a flattened multi-layer perceptron cascade structure. Spatially aware multi-layer perceptrons were embedded in the feature mapping layer to achieve high-precision inference. Experimental results show that Point-MLPBLS improves the mean Intersection-over-Union (mIoU) by 12.9 percentage points compared with PointNet++ on the S3DIS dataset, reduces the segmentation time by 41.1%, improves the efficiency of point cloud segmentation, and provides an efficient solution for 3D point cloud semantic segmentation.

Key words: 3D point cloud, semantic segmentation, broad learning system, feature fusion, attention mechanism