PointLGGS: Parallel Local-Global Dual-Path for Efficient Point Cloud Classification

Abstract

Despite significant advances in point cloud-based 3D vision research, existing methods often fail to achieve efficient collaborative modeling of both fine-grained local geometry and long-range global semantics, which limits further gains in classification accuracy. To address this limitation, this paper proposes a lightweight point cloud classification network named PointLGGS. Its three core contributions are summarized as follows. First, a Local Geometric Feature Extractor (LGFE) is designed to accurately capture fine-grained structures within local point cloud patches through relative position encoding and max-pooling operations. Second, a Global Semantic Feature Extractor (GSFE) is constructed by integrating dual mechanisms: Patch-Point Self-Attention (PP-SA) and Channel Self-Attention (CSA). This design jointly models long-range dependencies between patches and contextual correlations across channel dimensions, enabling efficient global semantic extraction. Third, using the LG-GS block as a fundamental building unit, a dual-path parallel architecture is employed to couple the LGFE and GSFE. Deep integration of local and global features is achieved via adaptive fusion, resulting in robust point cloud representations. Experimental results show that PointLGGS achieves classification accuracies of 94.0% and 90.7% on the ModelNet40 and ScanObjectNN datasets, respectively, with only 4.6 million parameters, thereby striking an excellent balance between classification accuracy and model efficiency.