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

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 \textbf{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.