Fine-grained point cloud classification based on hierarchical feature enhancement. Journal of Zhejiang University (Science Edition),2025,52(1):70⁃80(基于层次特征增强的细粒度点云分类)

Aiming at the problem of insufficient local feature extraction of general point cloud classification methods in fine-grained classification tasks, we propose a point cloud-oriented 3D model classification framework, HFE-Net. The Veronese mapping-based point feature enhancement module (V-PE) is used to enhance the point cloud data, so that the network learns higher-order information of the normal and the attitude; the multi-scale context-aware intra-cluster feature enhancement module (CA-IntraCE) utilizes different scales of K-nearest neighbor algorithms and cross-attention to achieve different scales of features and eliminate the loss of information caused by maximal pooling; the inter-cluster feature enhancement module (GSS-InterCE) based on grouped sparse sampling utilizes the furthest-point-sampling (FPS) algorithm to obtain sparse points and the cross-attention to achieve the enhancement of different clusters, so that the network has stronger fine-grained discriminative ability.In the experimental results