mlsanityData to 14 Oct 2026

SUM: A Benchmark Dataset of Semantic Urban Meshes

arXiv 2103.00355 ↗First posted 27 Feb 2021cs.CV cs.AIcited by 23

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Abstract (arXiv)

Recent developments in data acquisition technology allow us to collect 3D texture meshes quickly. Those can help us understand and analyse the urban environment, and as a consequence are useful for several applications like spatial analysis and urban planning. Semantic segmentation of texture meshes through deep learning methods can enhance this understanding, but it requires a lot of labelled data. The contributions of this work are threefold: (1) a new benchmark dataset of semantic urban meshes, (2) a novel semi-automatic annotation framework, and (3) an annotation tool for 3D meshes. In particular, our dataset covers about 4 km2 in Helsinki (Finland), with six classes, and we estimate that we save about 600 hours of labelling work using our annotation framework, which includes initial segmentation and interactive refinement. We also compare the performance of several state-of-theart 3D semantic segmentation methods on the new benchmark dataset. Other researchers can use our results to train their networks: the dataset is publicly available, and the annotation tool is released as open-source.

Key facts. Cited by 23 papers in the map (data through 14 Oct 2026). Builds on 14 of its 55 references that are in the map. First posted on arXiv 27 Feb 2021.

Builds on

14 of 55 references.

References · 14

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
  2. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,401
  3. 2019KPConv: Flexible and Deformable Convolution for Point Cloudscited by 1,205
  4. 2019RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Cloudscited by 585
  5. 2017Large-scale Point Cloud Semantic Segmentation with Superpoint Graphscited by 385
  6. 2017Semantic3D.net: A new Large-scale Point Cloud Classification Benchmarkcited by 158
  7. 2020Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challengescited by 80
  8. 2020Toronto-3D: A Large-scale Mobile LiDAR Dataset for Semantic Segmentation of Urban Roadwayscited by 77
  9. 2020DALES: A Large-scale Aerial LiDAR Data Set for Semantic Segmentationcited by 66
  10. 2017Paris-Lille-3D: a large and high-quality ground truth urban point cloud dataset for automatic segmentation and classificationcited by 65
  11. 2021The Hessigheim 3D (H3D) Benchmark on Semantic Segmentation of High-Resolution 3D Point Clouds and Textured Meshes from UAV LiDAR and Multi-View-Stereocited by 37
  12. 2019DublinCity: Annotated LiDAR Point Cloud and its Applicationscited by 31
2 more
  1. 2020Campus3D: A Photogrammetry Point Cloud Benchmark for Hierarchical Understanding of Outdoor Scenecited by 27
  2. 2020Semantic Segmentation on Swiss3DCities: A Benchmark Study on Aerial Photogrammetric 3D Pointcloud Datasetcited by 22

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 2; 2022: 3; 2023: 5; 2024: 2; 2025: 8; 2026: 3.

23 papers.

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  1. 2022EarthNets: Empowering AI in Earth Observationcited by 40
  2. 2023UrbanBIS: a Large-scale Benchmark for Fine-grained Urban Building Instance Segmentationcited by 24
  3. 2025NuiScene: Exploring Efficient Generation of Unbounded Outdoor Scenescited by 19
  4. 20213D Instance Segmentation of MVS Buildingscited by 12
  5. 2021Synthetic 3D Data Generation Pipeline for Geometric Deep Learning in Architecturecited by 7
  6. 2022PSSNet: Planarity-sensible Semantic Segmentation of Large-scale Urban Meshescited by 5
  7. 2023Learnable Earth Parser: Discovering 3D Prototypes in Aerial Scanscited by 5
  8. 2025TUM2TWIN: Introducing the Large-Scale Multimodal Urban Digital Twin Benchmark Datasetcited by 5
  9. 2023Human Vision Based 3D Point Cloud Semantic Segmentation of Large-Scale Outdoor Scenecited by 4
  10. 2025PG-SAG: Parallel Gaussian Splatting for Fine-Grained Large-Scale Urban Buildings Reconstruction via Semantic-Aware Groupingcited by 4
  11. 2023AutoEncoding Tree for City Generation and Applicationscited by 3
  12. 2024Beyond Grid Data: Exploring Graph Neural Networks for Earth Observationcited by 3
11 more
  1. 2025CitySeg: A 3D Open Vocabulary Semantic Segmentation Foundation Model in City-scale Scenarioscited by 2
  2. 2025SUM Parts: Benchmarking Part-Level Semantic Segmentation of Urban Meshescited by 1
  3. 2025Turin3D: Evaluating Adaptation Strategies under Label Scarcity in Urban LiDAR Segmentation with Semi-Supervised Techniquescited by 1
  4. 2025BuildingWorld: A Structured 3D Building Dataset for Urban Foundation Modelscited by 1
  5. 2022Effective Utilisation of Multiple Open-Source Datasets to Improve Generalisation Performance of Point Cloud Segmentation Modelscited by 0
  6. 2023Semantic Segmentation of Urban Textured Meshes Through Point Samplingcited by 0
  7. 2024LMSeg: An end-to-end geometric message-passing network on barycentric dual graphs for large-scale landscape mesh segmentationcited by 0
  8. 2025OpenUrban3D: Annotation-Free Open-Vocabulary Semantic Segmentation of Large-Scale Urban Point Cloudscited by 0
  9. 2026Semantic Segmentation of Textured Non-manifold 3D Meshes using Transformerscited by 0
  10. 2026COSTA: A Cluster-Centric Paradigm for Annotation-Free Open-Set Semantic Segmentation of Aerial Point Clouds with Domain Shiftscited by 0
  11. 2026Polis: 3D Self-Supervision at City Scalecited by 0