Textured meshes are becoming an increasingly popular representation combining the 3D geometry and radiometry of real scenes. However, semantic segmentation algorithms for urban mesh have been little investigated and do not exploit all radiometric information. To address this problem, we adopt an approach consisting in sampling a point cloud from the textured mesh, then using a point cloud semantic segmentation algorithm on this cloud, and finally using the obtained semantic to segment the initial mesh. In this paper, we study the influence of different parameters such as the sampling method, the density of the extracted cloud, the features selected (color, normal, elevation) as well as the number of points used at each training period. Our result outperforms the state-of-the-art on the SUM dataset, earning about 4 points in OA and 18 points in mIoU.

翻译：纹理网格正成为结合真实场景三维几何与辐射度的一种日益流行的表示形式。然而，针对城市网格的语义分割算法研究较少，且未能充分利用所有辐射信息。为解决这一问题，我们采用一种方法：首先从纹理网格中采样点云，然后对点云应用语义分割算法，最后利用获得的语义信息对原始网格进行分割。本文研究了不同参数的影响，包括采样方法、提取点云的密度、所选特征（颜色、法线、高程）以及每个训练周期使用的点数量。我们的结果在SUM数据集上超越了现有最佳水平，在总体精度（OA）上提升约4个百分点，在平均交并比（mIoU）上提升约18个百分点。