摘要 : Many previous works have achieved tremendous success for point cloud processing. However, they still suffer from inefficiency in memory and computation. In this paper, we introduce Lightweight Hierarchical Parallel Heterogeneous G... 展开 Many previous works have achieved tremendous success for point cloud processing. However, they still suffer from inefficiency in memory and computation. In this paper, we introduce Lightweight Hierarchical Parallel Heterogeneous Group Convolutional Neural Networks (LHPHGCNN), an efficient and lightweight neural architecture to achieve better performance but lower complexity than most existing methods for point cloud processing. By designing different local structure encodings, LHPHGCNN fully mines rich local geometric features. Additionally, we further propose Hierarchical Parallel Heterogeneous Group Convolution (HPHGConv) to simultaneously capture the discriminative nonlocal features and fine-grained local geometric features of point clouds in heterogeneous groups with fewer parameters and lower computing costs, which helps to recognize elusive shapes. To further capture the contextual features along with rich semantics, we introduce a novel multi-scale semantics (MSS) strategy to progressively increase the receptive field for each local area through the information communication between different scale areas. Extensive experiments show that our LHPHGCNN significantly outperforms state-of-the-art approaches for shape classification on ModelNet40 and semantic segmentation on three large scale benchmarks S3DIS, vKITTI, ScanNet, and SemanticKITTI in terms of accuracy and complexity. 收起