HB-Mamba: Hierarchical Bi-directional State Space Modeling for LiDAR Semantic Segmentation in Autonomous Driving

Engineering notes

Experimental results on the nuScenes-Lidarseg benchmark demonstrate that HB-Mamba achieves state-of-the-art performance among Lidar-only methods, reaching 82.8% mIoU on the test set and 81.33% mIoU on the validation set, outperforming the leading transformer-based model PTv3 by 0.1% and 1.01%, respectively.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

3D semantic segmentation remains a pivotal challenge for autonomous driving due to the inherent sparsity of points. Existing CNN-based and Transformer-based methods struggle with either limited receptive fields or quadratic computational complexity. Although some Mamba-based 3D models are designed efficiently with linear complexity, they often overlook the long-term decay problem in Selective State-space Models when processing extremely long sequences in large-scale scenes. In this paper, we propose a Hierarchical Bi-directional Mamba (HB-Mamba) for point cloud semantic segmentation. By decoupling feature extraction into a Global Memory branch and a Local Detail branch, our architecture effectively captures long-range semantics and preserves fine-grained geometric information. Besides, we further introduce a Spatial-Channel Fusion Block to dynamically fuse these multi-scale representations. Experimental results on the nuScenes-Lidarseg benchmark demonstrate that HB-Mamba achieves state-of-the-art performance among Lidar-only methods, reaching 82.8% mIoU on the test set and 81.33% mIoU on the validation set, outperforming the leading transformer-based model PTv3 by 0.1% and 1.01%, respectively.

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