提升点云语义分割边界精度，增强空间上下文感知能力。
SIESEF-FusionNet: Spatial Inter-correlation Enhancement and Spatially-Embedded Feature Fusion Network for LiDAR Point Cloud Semantic Segmentation
- 通过距离加权与角度补偿融合，增强空间相关性。
- 在Toronto3D上达83.7% mIoU，semanticKITTI上61.1% mIoU。
- 模块可即插即用，适合自动驾驶点云处理场景。
点云语义分割中不同语义类别的边界模糊常导致智能感知系统（如自动驾驶）误判。为此，本文提出SIESEF-FusionNet，通过结合逆距离加权与角度补偿来增强空间互相关性，提取更有效空间信息且不引入冗余。同时设计新型空间自适应池化模块，将增强的空间信息嵌入语义特征以提升上下文感知能力。实验表明，在Toronto3D数据集上达到83.7% mIoU和97.8% OA，优于其他基线方法；在semanticKITTI上实现61.1% mIoU，显著提升分割性能。消融实验进一步验证了所提模块的有效性与即插即用特性。
原文摘要 · Abstract (English)
The ambiguity at the boundaries of different semantic classes in point cloud semantic segmentation often leads to incorrect decisions in intelligent perception systems, such as autonomous driving. Hence, accurate delineation of the boundaries is crucial for improving safety in autonomous driving. A novel spatial inter-correlation enhancement and spatially-embedded feature fusion network (SIESEF-FusionNet) is proposed in this paper, enhancing spatial inter-correlation by combining inverse distance weighting and angular compensation to extract more beneficial spatial information without causing redundancy. Meanwhile, a new spatial adaptive pooling module is also designed, embedding enhanced spatial information into semantic features for strengthening the context-awareness of semantic features. Experimental results demonstrate that 83.7% mIoU and 97.8% OA are achieved by SIESEF-FusionNet on the Toronto3D dataset, with performance superior to other baseline methods. A value of 61.1% mIoU is reached on the semanticKITTI dataset, where a marked improvement in segmentation performance is observed. In addition, the effectiveness and plug-and-play capability of the proposed modules are further verified through ablation studies.
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