广东工业大学学报2025，Vol.42Issue(2)：29-36,80,9.
基于可分离Transformer的点云分类方法
Point Cloud Classification Based on Separable Transformer
摘要
Abstract
Transformer tends to take advantage of capturing remote dependencies to extract relational interactions at remote points of the point cloud,ignoring important local structural details,and achieves high performance by significantly increasing the computational burden.To alleviate this problem,we propose a separable Transformer point cloud classification method,named Sep-point,based on the idea of separable visual Transformer.The proposed Sep-point facilitates sequential local-global relational interactions within and between groups of point clouds through depth-separable self-attention.New location token embedding and group self-attention methods are used to compute inter-group attentional relationships with negligible computational cost and to establish telematic interactions across multiple regions,respectively.In this way,the local-global features are extracted while the computational burden is significantly reduced.Experimental results show that the proposed Sep-point improves the classification accuracy by 0.2%on the ModelNet40 dataset over the existing PCT(Point Cloud Transformer)and by 6.3%on the real ScanObjectNN dataset,respectively.Moreover,the number of network parameters and FLOPS metrics are reduced by 0.72M and 0.18G,respectively.These experimental results clearly demonstrate the promising effectiveness of our proposed method.关键词
点云分类/可分离Transformer/位置令牌嵌入/局部-全局关系交互Key words
point cloud classification/separable Transformer/location token embedding/local-global relational interactions分类
信息技术与安全科学引用本文复制引用
刘诚辉,李光平..基于可分离Transformer的点云分类方法[J].广东工业大学学报,2025,42(2):29-36,80,9.基金项目
国家自然科学基金资助项目(61601130) （61601130）
大亚湾科技规划项目(2020010203) （2020010203）
