The point cloud classification tasks face the dual challenge of efficiently extracting local geometric features while maintaining model complexity. The Mamba architecture utilizes the linear complexity advantage of state space models (SSMs) to overcome the computational bottleneck of Transformers while balancing global modeling capabilities. However, the inherent contradiction between its unidirectional dependency and the unordered nature of point clouds impedes modeling spatial correlation in local neighborhoods, thus constraining geometric feature extraction. This paper proposes Hybrid-Emba3D, a bidirectional Mamba model enhanced by geometry-feature coupling and cross-path feature hybridization. The Local geometric pooling with geometry-feature coupling mechanism significantly enhances local feature discriminative power via coordinated propagation and dynamic aggregation of geometric information between local center points and their neighborhoods, without introducing additional parameters. The designed Collaborative feature enhancer adopts dual-path hybridization, effectively handling local mutations and sparse key signals, breaking through the limitations of traditional SSM long-range modeling. Experimental results demonstrate that the proposed model achieves a new SOTA classification accuracy of 95.99% on ModelNet40 with only 0.03M additional.
View on arXiv@article{liu2025_2505.11099,
title={ Hybrid-Emba3D: Geometry-Aware and Cross-Path Feature Hybrid Enhanced State Space Model for Point Cloud Classification },
author={ Bin Liu and Chunyang Wang and Xuelian Liu and Guan Xi and Ge Zhang and Ziteng Yao and Mengxue Dong },
journal={arXiv preprint arXiv:2505.11099},
year={ 2025 }
}