  Transformers have demonstrated impressive results for 3D point cloud semantic
segmentation. However, the quadratic complexity of transformer makes
computation cost high, limiting the number of points that can be processed
simultaneously and impeding the modeling of long-range dependencies. Drawing
inspiration from the great potential of recent state space models (SSM) for
long sequence modeling, we introduce Mamba, a SSM-based architecture, to the
point cloud domain and propose Mamba24/8D, which has strong global modeling
capability under linear complexity. Specifically, to make disorderness of point
clouds fit in with the causal nature of Mamba, we propose a multi-path
serialization strategy applicable to point clouds. Besides, we propose the
ConvMamba block to compensate for the shortcomings of Mamba in modeling local
geometries and in unidirectional modeling. Mamba24/8D obtains state of the art
results on several 3D point cloud segmentation tasks, including ScanNet v2,
ScanNet200 and nuScenes, while its effectiveness is validated by extensive
experiments.
