Point Could Mamba: Point Cloud Learning via State Space Model

In this work, for the first time, we demonstrate that Mamba-based point cloud methods can outperform point-based methods. Mamba exhibits strong global modeling capabilities and linear computational complexity, making it highly attractive for point cloud analysis. To enable more effective processing of 3-D point cloud data by Mamba, we propose a novel Consistent Traverse Serialization to convert point clouds into 1-D point sequences while ensuring that neighboring points in the sequence are also spatially adjacent. Consistent Traverse Serialization yields six variants by permuting the order of x, y, and z coordinates, and the synergistic use of these variants aids Mamba in comprehensively observing point cloud data. Furthermore, to assist Mamba in handling point sequences with different orders more effectively, we introduce point prompts to inform Mamba of the sequence's arrangement rules. Finally, we propose positional encoding based on spatial coordinate mapping to inject positional information into point cloud sequences better. Based on these improvements, we construct a point cloud network named Point Cloud Mamba, which combines local and global modeling. Point Cloud Mamba surpasses the SOTA point-based method PointNeXt and achieves new SOTA performance on the ScanObjectNN, ModelNet40, and ShapeNetPart datasets.