Semantic Segmentation of Urban Textured Meshes Through Point Sampling

TL;DR

This paper proposes a novel approach for semantic segmentation of urban textured meshes by sampling point clouds and applying segmentation algorithms, significantly improving accuracy over existing methods.

Contribution

It introduces a point sampling-based framework for urban mesh segmentation that leverages radiometric information and studies parameter effects to enhance performance.

Findings

Outperforms state-of-the-art on SUM dataset with 4% OA improvement

Achieves 18% higher mIoU compared to previous methods

Analyzes impact of sampling parameters and features on segmentation accuracy

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.