Sparse Point Cloud Classification Method Based on MSE-Mamba

As a key task in LiDAR data processing, point cloud classification directly determines the accuracy and reliability of downstream applications such as autonomous driving and robot navigation. However, in practical scenarios, point cloud sparsity is easily affected by various factors, leading to a decrease in classification accuracy. To address this issue, this paper proposes a sparse point cloud classification method based on the MSE-Mamba neural network. By combining the efficient sequence processing advantages of the MSE-Mamba module with the global modeling capability of the global attention Transformer module, high-precision classification of sparse point clouds is achieved. Extensive experimental results on ModelNet40, ScanObjectNN, and self-built 3D imaging LiDAR point cloud datasets show that the proposed method exhibits excellent point cloud classification performance in various scenarios, with overall accuracy improved compared to current mainstream methods. It provides a new approach for high-precision classification of sparse point clouds and is of great significance for promoting the practical application of LiDAR technology in complex scenes.