SUM Parts: Benchmarking Part-Level Semantic Segmentation of Urban Meshes

Weixiao Gao, Liangliang Nan, Hugo Ledoux; Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR), 2025, pp. 24474-24484

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.

Related Material

[pdf] [supp] [arXiv]
[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} }