Point cloud classification network based on self-attention mechanism. (December 2022)

• Record Type: Journal Article
• Authors: Li, Yujie; Cai, Jintong
• Is Part Of: Computers & electrical engineering. Volume 104:Part A(2022)
• Publication Date: 2022-12
• Subjects: Local feature extraction -- Point cloud -- Point cloud classification -- Self-attention mechanism; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.108451
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: PointNet makes it possible to process point cloud data directly. However, PointNet only extracts global features and cannot capture fine local features. How to build a refined local feature extractor is the main goal of the research. Recently, Transformer has been used for point cloud processing tasks with better performance than other methods. We refer to Transformer and use the self-attention mechanism to design a refined feature extractor to capture richer feature information. In addition, we get the local geometric information at different scales with a local feature extraction module and use affine transformation to convert the local features to a normal distribution. We report results on the ModelNet40 dataset, new feature extraction network greatly improves classification tasks. Graphical abstract: Image, graphical abstract