Semantic segmentation in urban scene analysis has mainly focused on images or point clouds, while textured meshes - offering richer spatial representation - remain underexplored. This paper introduces SUM Parts, the first large-scale dataset for urban textured meshes with part-level semantic labels, covering about 2.5 km2 with 21 classes. The dataset was created using our own annotation tool, which supports both face- and texture-based annotations with efficient interactive selection. We also provide a comprehensive evaluation of 3D semantic segmentation and interactive annotation methods on this dataset. Our project page is available atthis https URL.
View on arXiv@article{gao2025_2503.15300,
title={ SUM Parts: Benchmarking Part-Level Semantic Segmentation of Urban Meshes },
author={ Weixiao Gao and Liangliang Nan and Hugo Ledoux },
journal={arXiv preprint arXiv:2503.15300},
year={ 2025 }
}