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arXiv Machine Learning By Chuang Chen

RaSFNet: Ray-aligned and Scan-aware Fusion Network for LiDAR Semantic Segmentation

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RaSFNet introduces a Ray-aligned and Scan-aware Fusion Network for LiDAR semantic segmentation, addressing limitations of projection-based methods by preserving geometric differences within each frustum and modeling spatial relationships across scanning regions. It employs a Ray-Aligned Geometric Distribution Encoding (RAGDE) module, a Scan-Topology-Aware Mamba (STAM) block, and a Range-Constrained Point-Frustum Fusion (RCPFF) mechanism to integrate point cloud geometry, LiDAR scanning characteristics, and range information. The network achieves competitive mIoU scores of 70.2% on SemanticKITTI and 77.7% on nuScenes validation sets.

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