提出MCNet网络，提升大规模室外点云语义分割精度。
Multilateral Cascading Network for Semantic Segmentation of Large-Scale Outdoor Point Clouds
- 采用多路级联注意力增强与点交叉阶段部分模块融合特征。
- 在SensatUrban上整体mIoU领先2.1%，小样本类别平均提升15.9%。
- 适合需要高精度点云分割的自动驾驶与城市建模场景。
大规模室外点云的语义分割在环境感知与场景理解中具有重要意义，但因真实环境中物体结构复杂、分布多样，仍面临巨大挑战。本文提出多向级联网络（MCNet），包含两个核心组件：多路级联注意力增强（MCAE）模块，通过多向级联操作学习复杂局部特征；点交叉阶段部分（P-CSP）模块，融合全局与局部特征，优化多尺度信息整合。在Toronto3D和SensatUrban两个主流基准数据集上，所提方法均优于现有先进方法。尤其在城市级规模的SensatUrban数据集上，整体mIoU较当前最优结果提升2.1%，对占比不足2%的小样本类别平均提升15.9%，显著优于基线方法。
原文摘要 · Abstract (English)
Semantic segmentation of large-scale outdoor point clouds is of significant importance in environment perception and scene understanding. However, this task continues to present a significant research challenge, due to the inherent complexity of outdoor objects and their diverse distributions in real-world environments. In this study, we propose the Multilateral Cascading Network (MCNet) designed to address this challenge. The model comprises two key components: a Multilateral Cascading Attention Enhancement (MCAE) module, which facilitates the learning of complex local features through multilateral cascading operations; and a Point Cross Stage Partial (P-CSP) module, which fuses global and local features, thereby optimizing the integration of valuable feature information across multiple scales. Our proposed method demonstrates superior performance relative to state-of-the-art approaches across two widely recognized benchmark datasets: Toronto3D and SensatUrban. Especially on the city-scale SensatUrban dataset, our results surpassed the current best result by 2.1\% in overall mIoU and yielded an improvement of 15.9\% on average for small-sample object categories comprising less than 2\% of the total samples, in comparison to the baseline method.
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