ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume V-2-2022
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2022, 177–184, 2022
https://doi.org/10.5194/isprs-annals-V-2-2022-177-2022
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2022, 177–184, 2022
https://doi.org/10.5194/isprs-annals-V-2-2022-177-2022
 
17 May 2022
17 May 2022

SEMANTIC SEGMENTATION OF URBAN TEXTURED MESHES THROUGH POINT SAMPLING

G. Grzeczkowicz1,2 and B. Vallet1 G. Grzeczkowicz and B. Vallet
  • 1LASTIG, Univ Gustave Eiffel, IGN, ENSG, France
  • 2Direction Générale de l’Armement, France

Keywords: Mesh, Semantic Segmentation, 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 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.