mlsanityData to 7 Oct 2026

Semantic Segmentation on Swiss3DCities: A Benchmark Study on Aerial Photogrammetric 3D Pointcloud Dataset

arXiv 2012.12996 ↗First posted 23 Dec 2020cs.CVcited by 22

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Abstract (arXiv)

We introduce a new outdoor urban 3D pointcloud dataset, covering a total area of 2.7 km2, sampled from three Swiss cities with different characteristics. The dataset is manually annotated for semantic segmentation with per-point labels, and is built using photogrammetry from images acquired by multirotors equipped with high-resolution cameras. In contrast to datasets acquired with ground LiDAR sensors, the resulting point clouds are uniformly dense and complete, and are useful to disparate applications, including autonomous driving, gaming and smart city planning. As a benchmark, we report quantitative results of PointNet++, an established point-based deep 3D semantic segmentation model; on this model, we additionally study the impact of using different cities for model generalization.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Dec 2020.

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Citing papers per year: 2021: 4; 2022: 4; 2023: 2; 2024: 6; 2025: 4; 2026: 2.

22 papers.

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  1. 2022STPLS3D: A Large-Scale Synthetic and Real Aerial Photogrammetry 3D Point Cloud Datasetcited by 52
  2. 2022EarthNets: Empowering AI in Earth Observationcited by 40
  3. 2023UrbanBIS: a Large-scale Benchmark for Fine-grained Urban Building Instance Segmentationcited by 24
  4. 2021SUM: A Benchmark Dataset of Semantic Urban Meshescited by 23
  5. 2022City3D: Large-Scale Building Reconstruction from Airborne LiDAR Point Cloudscited by 22
  6. 2024Conformal Semantic Image Segmentation: Post-hoc Quantification of Predictive Uncertaintycited by 19
  7. 2024A comprehensive overview of deep learning techniques for 3D point cloud classification and semantic segmentationcited by 14
  8. 20213D Instance Segmentation of MVS Buildingscited by 12
  9. 2024ECLAIR: A High-Fidelity Aerial LiDAR Dataset for Semantic Segmentationcited by 10
  10. 2021Efficient Urban-scale Point Clouds Segmentation with BEV Projectioncited by 6
  11. 2024Deep Learning on 3D Semantic Segmentation: A Detailed Reviewcited by 6
  12. 2021Applications of Explainable AI for 6G: Technical Aspects, Use Cases, and Research Challengescited by 5
10 more
  1. 2023HRHD-HK: A benchmark dataset of high-rise and high-density urban scenes for 3D semantic segmentation of photogrammetric point cloudscited by 2
  2. 2025CitySeg: A 3D Open Vocabulary Semantic Segmentation Foundation Model in City-scale Scenarioscited by 2
  3. 2024OpenTrench3D: A Photogrammetric 3D Point Cloud Dataset for Semantic Segmentation of Underground Utilitiescited by 1
  4. 2025SUM Parts: Benchmarking Part-Level Semantic Segmentation of Urban Meshescited by 1
  5. 2025Turin3D: Evaluating Adaptation Strategies under Label Scarcity in Urban LiDAR Segmentation with Semi-Supervised Techniquescited by 1
  6. 2022Effective Utilisation of Multiple Open-Source Datasets to Improve Generalisation Performance of Point Cloud Segmentation Modelscited by 0
  7. 2024WHU-Synthetic: A Synthetic Perception Dataset for 3-D Multitask Model Researchcited by 0
  8. 2025Ground Awareness in Deep Learning for Large Outdoor Point Cloud Segmentationcited by 0
  9. 2026Polis: 3D Self-Supervision at City Scalecited by 0
  10. 2026VoxelFix: Post-Hoc Semantic Correction of Completed 3D Voxel Mapscited by 0