Urban meshes semantic segmentation is essential for comprehending the 3D real-world environments, as it plays a vital role across various application domains, including digital twins, 3D navigation, and smart cities. Nevertheless, the inherent topological complexities of urban meshes impede the precise representation of dependencies and local structures, yielding compromised segmentation accuracy, especially for small or irregularly-shaped objects like vegetation and vehicles. To address this challenge, we introduce UrbanSegNet, a novel end-to-end model incorporating diffusion perceptron blocks and a vertex spatial attention mechanism. The diffusion perceptron blocks can dynamically enlarge receptive fields to capture features from local to completely global, enabling effective representation of urban meshes using multi-scale features and increasing small and irregularly-shaped object segmentation accuracy. The vertex spatial attention mechanism extracts the internal correlations within urban meshes to enhance semantic segmentation performance. Besides, a tailored loss function is designed to enhance overall performance further. Comprehensive experiments on two datasets demonstrate that the proposed method outperforms the state-of-the-art models in terms of mean F1 score, recall, and mean intersection over union (mIoU). The experimental results also demonstrate that UrbanSegNet achieves higher segmentation accuracy on vehicles and high vegetation compared to the state-of-the-art methods, highlighting the superiority of our proposed model in extracting features of small and irregularly-shaped objects.

中文翻译：

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UrbanSegNet：使用扩散感知器和顶点空间注意力的城市网格语义分割网络


城市网格语义分割对于理解 3D 现实世界环境至关重要，因为它在数字孪生、3D 导航和智能城市等各种应用领域中发挥着至关重要的作用。然而，城市网格固有的拓扑复杂性阻碍了依赖性和局部结构的精确表示，从而导致分割精度受损，特别是对于植被和车辆等小型或不规则形状的物体。为了应对这一挑战，我们引入了 UrbanSegNet，这是一种新颖的端到端模型，结合了扩散感知器块和顶点空间注意机制。扩散感知器模块可以动态扩大感受野，以捕获从局部到完全全局的特征，从而能够使用多尺度特征有效地表示城市网格，并提高小型和不规则形状的对象分割精度。顶点空间注意力机制提取城市网格内的内部相关性以增强语义分割性能。此外，定制的损失函数旨在进一步提高整体性能。对两个数据集的综合实验表明，所提出的方法在平均 F1 分数、召回率和平均交并集 (mIoU) 方面优于最先进的模型。实验结果还表明，与最先进的方法相比，UrbanSegNet 在车辆和高植被上实现了更高的分割精度，凸显了我们提出的模型在提取小型和不规则形状物体特征方面的优越性。