HyMamba: Mamba with Hybrid Geometry-Feature Coupling for Efficient Point Cloud Classification

• URL: http://arxiv.org/abs/2505.11099v2
• Date: Tue, 17 Jun 2025 04:40:21 GMT
• Title: HyMamba: Mamba with Hybrid Geometry-Feature Coupling for Efficient Point Cloud Classification
• Authors: Bin Liu, Chunyang Wang, Xuelian Liu, Bo Xiao, Guan Xi,
• Abstract summary: HyMamba is a geometry and feature coupled Mamba framework featuring: (1) Geometry-Feature Coupled Pooling (GFCP), which dynamically aggregating adjacent geometric information into local features; (2) Collaborative Feature Enhancer (CoFE), which enhances sparse signal capture through cross-path feature hybridization;.<n>The proposed model achieves superior classification performance, particularly on the ModelNet40 dataset, where it elevates accuracy to 95.99% with merely 0.03M additional parameters. Furthermore, it attains 98.9% accuracy on the ModelNetShot dataset, validating its robust generalization capabilities under sparse samples.
• Score: 7.139631485661567
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Point cloud classification is one of the essential technologies for achieving intelligent perception of 3D environments by machines, its core challenge is to efficiently extract local and global features. Mamba leverages state space models (SSMs) for global point cloud modeling. Although prior Mamba-based point cloud processing methods pay attention to the limitation of its flattened sequence modeling mechanism in fusing local and global features, the critical issue of weakened local geometric relevance caused by decoupling geometric structures and features in the input patches remains not fully revealed, and both jointly limit local feature extraction. Therefore, we propose HyMamba, a geometry and feature coupled Mamba framework featuring: (1) Geometry-Feature Coupled Pooling (GFCP), which achieves physically interpretable geometric information coupling by dynamically aggregating adjacent geometric information into local features; (2) Collaborative Feature Enhancer (CoFE), which enhances sparse signal capture through cross-path feature hybridization while effectively integrating global and local contexts. We conducted extensive experiments on ModelNet40 and ScanObjectNN datasets. The results demonstrate that the proposed model achieves superior classification performance, particularly on the ModelNet40, where it elevates accuracy to 95.99% with merely 0.03M additional parameters. Furthermore, it attains 98.9% accuracy on the ModelNetFewShot dataset, validating its robust generalization capabilities under sparse samples. Our code and weights are available at https://github.com/L1277471578/HyMamba

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