CloudMamba: Grouped Selective State Spaces for Point Cloud Analysis

CloudMamba, introduced in a recent arXiv publication, is designed to enhance point cloud analysis by overcoming limitations seen in previous models like Mamba. While Mamba is recognized for its long-range modeling ability and linear complexity, it still faced issues with point cloud serialization and overfitting. CloudMamba addresses these challenges through innovative techniques such as sequence expanding and merging, which allow for better handling of unordered point sets. Additionally, the grouped selective state space model (GS6) is proposed to mitigate overfitting by sharing parameters. Experiments conducted on various point cloud tasks demonstrate that CloudMamba achieves state-of-the-art results while maintaining significantly less complexity, marking a notable advancement in the field of computer vision.