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

Source: [SUM Parts: Benchmarking Part-Level Semantic Segmentation of Urban Meshes](<https://www.sota2.com/research/paper/68fa77941e209131cea741c8>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-12-25T21:13:05.148Z

Authors: Weixiao Gao, Liangliang Nan, Hugo Ledoux

Published: 2025-03-19T15:22:23.000Z

## 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 2.5 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 at https://tudelft3d.github.io/SUMParts/.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2503.15300>)

[PDF](<https://arxiv.org/pdf/2503.15300.pdf>)

[Implementation](<https://tudelft3d.github.io/SUMParts/>)