Initial upload: 3DSES indoor TLS dataset (Gold/Silver/Bronze/test_area)

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README.md

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----
-license: cc-by-4.0
----
+---
+license: cc-by-sa-4.0
+task_categories:
+  - token-classification
+tags:
+  - point-cloud
+  - lidar
+  - 3d
+  - semantic-segmentation
+  - indoor
+  - terrestrial-laser-scanning
+  - BIM
+pretty_name: "3DSES: Indoor TLS Point Cloud Segmentation"
+size_categories:
+  - 100M<n<1B
+---
+
+# 3DSES: Indoor TLS Point Cloud Segmentation Dataset
+
+![Illustration](Illustration.png)
+
+This repository provides a **mirror of the 3DSES dataset** to facilitate access
+for research and benchmarking purposes, with per-tier folders for selective downloads.
+
+> **Disclaimer**
+> This dataset is **not owned nor created by the maintainer of this Hugging Face repository**.
+> The **official and authoritative source** is the original Zenodo record:
+> https://zenodo.org/records/13323342
+
+**3DSES** (3D Segmentation of ESGT point clouds) is a dataset of dense indoor
+Terrestrial Laser Scanning (TLS) colorized point clouds covering **427 m²** of an
+engineering school (ESGT, Le Mans, France). It features dual annotation: semantic
+labels at the point level alongside a complete 3D CAD building model.
+
+## Dataset Summary
+
+| | Stations | Points | Intensity | Manual labels | Pseudo-labels | Columns | Size |
+|---|---|---|---|---|---|---|---|
+| **Gold** | 7 | 44.5M | Yes | Yes | Yes | 9 | 3.0 GB |
+| **Silver** | 27 | 195.5M | Yes | Yes | Yes | 9 | 14 GB |
+| **Bronze** | 39 | 384.9M | Yes | No | Yes | 8 | 23 GB |
+| **test_area** | 3 | 20.7M | No | No | Yes | 7 | 1.1 GB |
+
+**Total: ~645M points across 39 unique scan stations.**
+
+## File Format
+
+Each `.npy` file contains one scan station as a NumPy array (`float64`).
+
+**Gold / Silver (9 columns):**
+
+| Column | Content |
+|--------|---------|
+| 0-2 | X, Y, Z (local survey coordinates) |
+| 3-5 | R, G, B (0-255) |
+| 6 | Intensity (normalized 0-1) |
+| 7 | Manual semantic label |
+| 8 | Pseudo-label (from CAD model alignment) |
+
+**Bronze (8 columns):** same as above without column 7 (no manual labels).
+
+**test_area (7 columns):** X, Y, Z, R, G, B, pseudo-label only.
+
+## Directory Structure
+
+```
+Gold/           # 7 stations with full annotations
+  S163.npy ... S179.npy
+Silver/         # 27 stations with full annotations
+  S150.npy ... S179.npy, S295.npy, S296.npy
+Bronze/         # 39 stations with pseudo-labels only
+  S140.npy ... S179.npy, S295.npy, S296.npy
+test_area/      # 3 held-out stations for evaluation
+  S170.npy, S171.npy, S180.npy
+3DSES_cad_model.obj   # 3D CAD building model (320 MB)
+Illustration.png      # Dataset illustration
+```
+
+## Usage
+
+```python
+import numpy as np
+from pathlib import Path
+
+# Load a single station
+data = np.load("Gold/S163.npy")
+xyz = data[:, :3]          # coordinates
+rgb = data[:, 3:6]         # color (0-255)
+intensity = data[:, 6]     # normalized intensity
+labels = data[:, 7]        # manual semantic labels
+pseudo = data[:, 8]        # pseudo-labels from CAD
+
+# Load with projax3d
+from projax3d.opendata import get_provider
+provider = get_provider("3dses")
+scene = provider.load_scene("gold", output_dir="./data/3dses")
+```
+
+## Tiers
+
+- **Gold**: Stations with the highest annotation quality (manually verified labels
+  + pseudo-labels + intensity). Best for training and evaluation.
+- **Silver**: Larger set with manual labels. Superset of Gold stations.
+- **Bronze**: All stations with pseudo-labels only (automatically generated from
+  CAD model alignment, >95% accuracy). Largest coverage.
+- **test_area**: Held-out stations for benchmarking (pseudo-labels only).
+
+## Citation
+
+```bibtex
+@inproceedings{merizette2025_3dses,
+  title     = {{3DSES}: an indoor Lidar point cloud segmentation dataset
+               with real and pseudo-labels from a {3D} model},
+  author    = {M{\'e}rizette, Maxime and Audebert, Nicolas and
+               Kervella, Pierre and Verdun, J{\'e}r{\^o}me},
+  booktitle = {Proceedings of the 20th International Joint Conference on
+               Computer Vision, Imaging and Computer Graphics Theory and
+               Applications (VISAPP)},
+  year      = {2025},
+  address   = {Porto, Portugal},
+  note      = {arXiv:2501.17534}
+}
+```
+
+## License
+
+This dataset is distributed under the
+[Creative Commons Attribution Share Alike 4.0 International (CC-BY-SA 4.0)](https://creativecommons.org/licenses/by-sa/4.0/legalcode)
+license.
+
+## Links
+
+- **Paper**: [arXiv:2501.17534](https://arxiv.org/abs/2501.17534)
+- **Original dataset**: [Zenodo (DOI: 10.5281/zenodo.13323342)](https://zenodo.org/records/13323342)
+- **Code**: [github.com/merizetm/3dses](https://github.com/merizetm/3dses)
+- **Benchmark**: [Codabench competition](https://www.codabench.org/competitions/6927/)
+
+## Authors
+
+- Maxime Merizette (ESGT / QUARTA)
+- Nicolas Audebert (LaSTIG, IGN-ENSG)
+- Pierre Kervella (QUARTA / ESGT)
+- Jerome Verdun (ESGT)
+
+**Contributors:** Lea Corduri, Judicaelle Djeudji Tchaptchet, Damien Richard,
+Lilian Ribet, Elisabeth Simonetto.