mlsanityData to 7 Oct 2026

The Hessigheim 3D (H3D) Benchmark on Semantic Segmentation of High-Resolution 3D Point Clouds and Textured Meshes from UAV LiDAR and Multi-View-Stereo

arXiv 2102.05346 ↗First posted 10 Feb 2021cs.CVcited by 37

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Abstract (arXiv)

Automated semantic segmentation and object detection are of great importance in geospatial data analysis. However, supervised machine learning systems such as convolutional neural networks require large corpora of annotated training data. Especially in the geospatial domain, such datasets are quite scarce. Within this paper, we aim to alleviate this issue by introducing a new annotated 3D dataset that is unique in three ways: i) The dataset consists of both an Unmanned Aerial Vehicle (UAV) laser scanning point cloud and a 3D textured mesh. ii) The point cloud features a mean point density of about 800 pts/sqm and the oblique imagery used for 3D mesh texturing realizes a ground sampling distance of about 2-3 cm. This enables the identification of fine-grained structures and represents the state of the art in UAV-based mapping. iii) Both data modalities will be published for a total of three epochs allowing applications such as change detection. The dataset depicts the village of Hessigheim (Germany), henceforth referred to as H3D. It is designed to promote research in the field of 3D data analysis on one hand and to evaluate and rank existing and emerging approaches for semantic segmentation of both data modalities on the other hand. Ultimately, we hope that H3D will become a widely used benchmark dataset in company with the well-established ISPRS Vaihingen 3D Semantic Labeling Challenge benchmark (V3D). The dataset can be downloaded from https://ifpwww.ifp.uni-stuttgart.de/benchmark/hessigheim/default.aspx.

Key facts. Cited by 37 papers in the map (data through 7 Oct 2026). Builds on 9 of its 43 references that are in the map. First posted on arXiv 10 Feb 2021.

Builds on

9 of 43 references.

References · 9

  1. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,485
  2. 2017ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenescited by 3,088
  3. 20173D Semantic Segmentation with Submanifold Sparse Convolutional Networkscited by 746
  4. 2017Semantic3D.net: A new Large-scale Point Cloud Classification Benchmarkcited by 158
  5. 2020Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challengescited by 80
  6. 2017Paris-Lille-3D: a large and high-quality ground truth urban point cloud dataset for automatic segmentation and classificationcited by 65
  7. 2020DALES: A Large-scale Aerial LiDAR Data Set for Semantic Segmentationcited by 65
  8. 2019DublinCity: Annotated LiDAR Point Cloud and its Applicationscited by 31
  9. 2019A review on deep learning techniques for 3D sensed data classificationcited by 18

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 6; 2023: 3; 2024: 7; 2025: 10; 2026: 8.

37 papers.

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  1. 2022STPLS3D: A Large-Scale Synthetic and Real Aerial Photogrammetry 3D Point Cloud Datasetcited by 52
  2. 2022SensatUrban: Learning Semantics from Urban-Scale Photogrammetric Point Cloudscited by 49
  3. 2022EarthNets: Empowering AI in Earth Observationcited by 40
  4. 2025UAVScenes: A Multi-Modal Dataset for UAVscited by 26
  5. 2023UrbanBIS: a Large-scale Benchmark for Fine-grained Urban Building Instance Segmentationcited by 24
  6. 2021SUM: A Benchmark Dataset of Semantic Urban Meshescited by 23
  7. 2024GauU-Scene V2: Assessing the Reliability of Image-Based Metrics with Expansive Lidar Image Dataset Using 3DGS and NeRFcited by 21
  8. 2024A comprehensive overview of deep learning techniques for 3D point cloud classification and semantic segmentationcited by 14
  9. 2021A new weakly supervised approach for ALS point cloud semantic segmentationcited by 12
  10. 20213D Instance Segmentation of MVS Buildingscited by 12
  11. 2024FRACTAL: An Ultra-Large-Scale Aerial Lidar Dataset for 3D Semantic Segmentation of Diverse Landscapescited by 11
  12. 2024Deep Learning on 3D Semantic Segmentation: A Detailed Reviewcited by 6
25 more
  1. 2022PSSNet: Planarity-sensible Semantic Segmentation of Large-scale Urban Meshescited by 5
  2. 2022One Class One Click: Quasi Scene-level Weakly Supervised Point Cloud Semantic Segmentation with Active Learningcited by 5
  3. 2025TUM2TWIN: Introducing the Large-Scale Multimodal Urban Digital Twin Benchmark Datasetcited by 5
  4. 2025LiDAR Remote Sensing Meets Weak Supervision: Concepts, Methods, and Perspectivescited by 4
  5. 2023DC3DCD: unsupervised learning for multiclass 3D point cloud change detectioncited by 3
  6. 20253DSES: an indoor Lidar point cloud segmentation dataset with real and pseudo-labels from a 3D modelcited by 3
  7. 2023HRHD-HK: A benchmark dataset of high-rise and high-density urban scenes for 3D semantic segmentation of photogrammetric point cloudscited by 2
  8. 2025APCoTTA: Continual Test-Time Adaptation for Semantic Segmentation of Airborne LiDAR Point Cloudscited by 2
  9. 2025CitySeg: A 3D Open Vocabulary Semantic Segmentation Foundation Model in City-scale Scenarioscited by 2
  10. 2026AerialMetric: Benchmarking and Adapting UAV Monocular Metric Depth Estimation in the Real Worldcited by 2
  11. 2024OpenTrench3D: A Photogrammetric 3D Point Cloud Dataset for Semantic Segmentation of Underground Utilitiescited by 1
  12. 2024Depth Supervised Neural Surface Reconstruction from Airborne Imagerycited by 1
  13. 2025SUM Parts: Benchmarking Part-Level Semantic Segmentation of Urban Meshescited by 1
  14. 2025Turin3D: Evaluating Adaptation Strategies under Label Scarcity in Urban LiDAR Segmentation with Semi-Supervised Techniquescited by 1
  15. 2026Source-Free Domain Adaptation for Geospatial Point Cloud Semantic Segmentationcited by 1
  16. 2022Effective Utilisation of Multiple Open-Source Datasets to Improve Generalisation Performance of Point Cloud Segmentation Modelscited by 0
  17. 2024LMSeg: An end-to-end geometric message-passing network on barycentric dual graphs for large-scale landscape mesh segmentationcited by 0
  18. 2025Ground Awareness in Deep Learning for Large Outdoor Point Cloud Segmentationcited by 0
  19. 2025OpenUrban3D: Annotation-Free Open-Vocabulary Semantic Segmentation of Large-Scale Urban Point Cloudscited by 0
  20. 2026GridNet-HD: A High-Resolution Multi-Modal Dataset for LiDAR-Image Fusion on Power Line Infrastructurecited by 0
  21. 2026SOAR: Regression-based LiDAR Relocalization for UAVscited by 0
  22. 2026Cross-view Domain Generalization via Geometric Consistency for LiDAR Semantic Segmentationcited by 0
  23. 2026Hold-Out Self-Validation Cannot Certify Photogrammetric Accuracy: Saturation and Blindness to Coherent Distortioncited by 0
  24. 2026COSTA: A Cluster-Centric Paradigm for Annotation-Free Open-Set Semantic Segmentation of Aerial Point Clouds with Domain Shiftscited by 0
  25. 2026Polis: 3D Self-Supervision at City Scalecited by 0