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.

翻译：点云语义分割中不同语义类别边界处的模糊性常导致智能感知系统（如自动驾驶）做出错误决策。因此，精确划定边界对于提升自动驾驶的安全性至关重要。本文提出了一种新颖的空间互相关增强与空间嵌入特征融合网络（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，从而提取更具益处的空间信息且不引入冗余。同时，本文还设计了一种新的空间自适应池化模块，将增强后的空间信息嵌入语义特征中，以强化语义特征的上下文感知能力。实验结果表明，SIESEF-FusionNet在Toronto3D数据集上实现了83.7%的mIoU和97.8%的OA，其性能优于其他基线方法。在semanticKITTI数据集上达到了61.1%的mIoU，分割性能显著提升。此外，通过消融研究进一步验证了所提模块的有效性与即插即用能力。