RaSFNet 提出：面向 LiDAR 点云语义分割的射线对齐融合网络
RaSFNet: Ray-aligned and Scan-aware Fusion Network for LiDAR Semantic Segmentation
一篇 LiDAR 语义分割新论文，把射线几何和扫描模式融进投影方法，SemanticKITTI 跑到 70.2% mIoU，做自动驾驶感知的可以看看。
arXiv 论文 RaSFNet 针对 LiDAR 点云语义分割中投影方法的问题，提出 Ray-Aligned Geometric Distribution Encoding（RAGDE）模块，用射线对齐坐标系刻画每个视锥内的点分布。其 Scan-Topology-Aware Mamba（STAM）模块按 LiDAR 方位角采样模式组织特征，跨扫描区域捕获长距离依赖；Range-Constrained Point-Frustum Fusion（RCPFF）机制则用共享距离信息约束点特征与投影特征的交互。RaSFNet 在 SemanticKITTI 和 nuScenes 验证集上分别取得 70.2% 和 77.7% 的 mIoU，与所评估方法相比具有竞争力。
RaSFNet: Ray-aligned and Scan-aware Fusion Network for LiDAR Semantic Segmentation
LiDAR point cloud semantic segmentation is a fundamental component of 3D perception for autonomous driving and robotics. Projection-based methods map 3D point clouds into 2D representations for feature extraction using 2D networks. However, aggregating multiple points during projection can obscure geometric differences within each frustum, while local feature extraction limits the modeling of spatial relationships across scanning regions. Moreover, proximity in the projected representation does not necessarily imply proximity in 3D space, and interactions between point and projected features may mix features from different ranges, reducing semantic discriminability. To address these challenges, we propose the Ray-aligned and Scan-aware Fusion Network (RaSFNet), which incorporates point cloud geometry and LiDAR scanning characteristics into projection-based semantic segmentation. Specifically, the Ray-Aligned Geometric Distribution Encoding (RAGDE) module characterizes point distributions within each frustum using a ray-aligned coordinate system. To model spatial context, a Scan-Topology-Aware Mamba (STAM) block organizes frustum features according to the LiDAR azimuth sampling pattern and captures long-range dependencies across scanning regions. In addition, a Range-Constrained Point-Frustum Fusion (RCPFF) mechanism uses shared range information to guide feature exchange between point and frustum representations while maintaining spatial correspondence. RaSFNet achieves mIoU scores of 70.2% and 77.7% on the SemanticKITTI and nuScenes validation sets, respectively, indicating competitive segmentation performance compared with the evaluated methods.