Point Could Mamba: Point Cloud Learning via State Space Model

arXiv:2403.00762v1 Announce Type: new
Abstract: 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. …

3-d abstract analysis arxiv capabilities cloud complexity computational cs.cv global linear making mamba modeling processing space state type via work