SEMANTIC SEGMENTATION OF URBAN TEXTURED MESHES THROUGH POINT SAMPLING

Abstract. 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 do not exploit all radiometric information. To address this problem, we adopt approach consisting in sampling a point cloud from textured mesh, then using algorithm on cloud, finally obtained to segment initial mesh. In paper, study influence different parameters such as method, density extracted features selected (color, normal, elevation) well number points used at each training period. Our result outperforms state-of-the-art SUM dataset, earning about 4 OA 18 mIoU.