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

Semantic Segmentation of Urban Textured Meshes Through Point Sampling