# Semantic Segmentation of Urban Textured Meshes Through Point Sampling

Source: [Semantic Segmentation of Urban Textured Meshes Through Point Sampling](<https://www.sota2.com/research/paper/68fa6e9a1e209131cea41049>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T01:01:51.507Z

Authors: Gr\\'egoire Grzeczkowicz, Bruno Vallet (1) \_\_INSTITUTION\_2\_\_ LASTIG, Univ Gustave Eiffel, IGN, ENSG, (2) Direction G\\'en\\'erale de l'Armement)

Published: 2023-02-21T12:49:31.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2302.10635>)

[PDF](<https://arxiv.org/pdf/2302.10635.pdf>)