CVPR 2025 · poster
SUM Parts: Benchmarking Part-Level Semantic Segmentation of Urban Meshes
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.
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