  Point cloud segmentation is crucial for robotic visual perception and
environmental understanding, enabling applications such as robotic navigation
and 3D reconstruction. However, handling the sparse and unordered nature of
point cloud data presents challenges for efficient and accurate segmentation.
Inspired by the Mamba model's success in natural language processing, we
propose the Serialized Point Cloud Mamba Segmentation Model (Serialized Point
Mamba), which leverages a state-space model to dynamically compress sequences,
reduce memory usage, and enhance computational efficiency. Serialized Point
Mamba integrates local-global modeling capabilities with linear complexity,
achieving state-of-the-art performance on both indoor and outdoor datasets.
This approach includes novel techniques such as staged point cloud sequence
learning, grid pooling, and Conditional Positional Encoding, facilitating
effective segmentation across diverse point cloud tasks. Our method achieved
76.8 mIoU on Scannet and 70.3 mIoU on S3DIS. In Scannetv2 instance
segmentation, it recorded 40.0 mAP. It also had the lowest latency and
reasonable memory use, making it the SOTA among point semantic segmentation
models based on mamba.
