Recent developments in data acquisition technology allow us to collect 3D texture meshes quickly. Those can help us understand and analyse the urban environment, and as a consequence are useful for several applications like spatial analysis and urban planning. Semantic segmentation of texture meshes through deep learning methods can enhance this understanding, but it requires a lot of labelled data. The contributions of this work are three-fold: (1) a new benchmark dataset of semantic urban meshes, (2) a novel semi-automatic annotation framework, and (3) an annotation tool for 3D meshes. In particular, our dataset covers about 4 $k{m}^2$ in Helsinki (Finland), with six classes, and we estimate that we save about 600 h of labelling work using our annotation framework, which includes initial segmentation and interactive refinement. We also compare the performance of several state-of-the-art 3D semantic segmentation methods on the new benchmark dataset. Other researchers can use our results to train their networks: the dataset is publicly available, and the annotation tool is released as open-source.

数据采集​​技术的最新发展使我们能够快速采集 3D 纹理网格。这些可以帮助我们理解和分析城市环境，因此对空间分析和城市规划等多种应用很有用。通过深度学习方法对纹理网格进行语义分割可以增强这种理解，但它需要大量标记数据。这项工作的贡献有三方面：(1) 语义城市网格的新基准数据集，(2) 一种新颖的半自动注释框架，以及 (3) 3D 网格的注释工具。特别是，我们的数据集涵盖了大约 4$克{米}^2$在赫尔辛基（芬兰），有六个类，我们估计使用我们的注释框架可以节省大约 600 小时的标记工作，其中包括初始分割和交互式细化。我们还在新的基准数据集上比较了几种最先进的 3D 语义分割方法的性能。其他研究人员可以使用我们的结果来训练他们的网络：数据集是公开可用的，并且注释工具作为开源发布。