Papers › Serialized Point Mamba: A Serialized Point Cloud Mamba Segmentation Model

Serialized Point Mamba: A Serialized Point Cloud Mamba Segmentation Model

17 Jul 2024arXiv:2407.12319archive 2025-07-28

Tao Wang, Wei Wen, Jingzhi Zhai, Kang Xu, Haoming Luo

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.

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Tasks

3D Instance Segmentation 3D Reconstruction 3D Semantic Segmentation Computational Efficiency Instance Segmentation LIDAR Semantic Segmentation Mamba Point Cloud Segmentation Segmentation Semantic Segmentation

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
3D Instance Segmentation	ScanNet(v2)	Searilized Point Mamba	mAP	40.0	#31 of 32	Archive leaderboard	report
3D Instance Segmentation	ScanNet(v2)	Searilized Point Mamba	mAP@25	76.4	#31 of 32	Archive leaderboard	report
LIDAR Semantic Segmentation	nuScenes	Serialized Piont Mamba	val mIoU	0.806	#31 of 36	Archive leaderboard	report
Semantic Segmentation	S3DIS Area5	Serialized Piont Mamba	mIoU	70.6	#31 of 61	Archive leaderboard	report
Semantic Segmentation	ScanNet	Serialized Piont Mamba	val mIoU	76.8	#10 of 45	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Conditional Positional Encoding Depthwise Convolution Positional Encoding Generator

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