CVPR 2025poster0 citations
SUM Parts: Benchmarking Part-Level Semantic Segmentation of Urban Meshes
Weixiao Gao, Liangliang Nan, Hugo Ledoux
Abstract
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 2.5km^2 with 21 classes. The dataset was created using our designed annotation tool, supporting 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.
BibTeX
@InProceedings{Gao_2025_CVPR,
author = {Gao, Weixiao and Nan, Liangliang and Ledoux, Hugo},
title = {SUM Parts: Benchmarking Part-Level Semantic Segmentation of Urban Meshes},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {24474-24484}
}