README.md

---
license: cc-by-nc-sa-4.0
viewer: false
---

# DeKH — German Hospital Dataset

[![License: CC BY-NC-SA 4.0](https://img.shields.io/badge/License-CC%20BY--NC--SA%204.0-lightgrey.svg)](https://creativecommons.org/licenses/by-nc-sa/4.0/) [![arXiv](https://img.shields.io/badge/arXiv-2604.24311-b31b1b.svg)](https://arxiv.org/abs/2604.24311) [![EC3 2026](https://img.shields.io/badge/EC3-2026-blue.svg)](https://arxiv.org/abs/2604.24311)

**DeKH** (German Hospital Dataset) is a multi-scene dataset of real hospital environments comprising high-resolution 3D point clouds with semantic annotations and ground-truth IFC BIM models. It is introduced alongside **BIMStruct3D**, a fully automated hybrid Scan-to-BIM pipeline accepted at EC3 2026.

![Overview](overview.png)

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## Dataset Contents

The dataset covers four distinct hospital scenes across three buildings:

```
Buildings/
├── A/
│   ├── 1st_floor/
│   │   ├── DeKH_A_1st_floor.laz
│   │   └── DeKH_A_1st_floor.npy
│   ├── 2nd_floor/
│   │   ├── DeKH_A_2nd_floor.laz
│   │   └── DeKH_A_2nd_floor.npy
│   └── DeKH_A.ifc
├── B/
│   ├── DeKH_B_ICU.laz
│   ├── DeKH_B_ICU.npy
│   └── DeKH_B_ICU.ifc
└── C/
    ├── DeKH_C_surgery.laz
    ├── DeKH_C_surgery.npy
    └── DeKH_C_surgery.ifc
```

### File Formats

- **`.laz`** — Compressed point cloud (LAS/LAZ format). Each file contains the raw 3D point positions of the scanned environment.
- **`.npy`** — NumPy array of per-point semantic labels aligned to the corresponding `.laz` point cloud (`float32`, shape `(N,)`).
- **`.ifc`** — Ground-truth Building Information Model in the IFC standard, usable as reference for Scan-to-BIM evaluation.

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## Requirements

To load the point clouds and labels you need:

- **[laspy](https://laspy.readthedocs.io/en/latest/)** with LAZ support (via `lazrs` or `laszip` backend):
  ```bash
  pip install laspy[lazrs]
  ```
  See the [laspy installation guide](https://laspy.readthedocs.io/en/latest/installation.html) for details on LAZ backends.

- **[NumPy](https://numpy.org/)**:
  ```bash
  pip install numpy
  ```

### Quick Start

```python
import laspy
import numpy as np

# Load point cloud
las = laspy.read("Buildings/B/DeKH_B_ICU.laz")
points = np.vstack([las.x, las.y, las.z]).T  # (N, 3)

# Load semantic labels
labels = np.load("Buildings/B/DeKH_B_ICU.npy")  # (N,) float32
```

---

## Annotation

Semantic labels were produced following the ontology and labeling methodology introduced in:

> F. Kaufmann, M. Chamseddine, S. Guttikonda, C. Glock, D. Stricker, J. Rambach, [*"Ontology-Based Semantic Labeling for RGB-D and Point Cloud Datasets"*](https://ec-3.org/wp-content/uploads/2025/10/EC32023_241.pdf), EC3 2023.

---

## License

This dataset is released under [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) (Attribution-NonCommercial-ShareAlike). It may not be used for commercial purposes.

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## Citing this Work

If you use DeKH in your research, please cite:

```bibtex
@article{chamseddine2026bimstruct3d,
    title   = {BIMStruct3D: A Fully Automated Hybrid Learning Scan-to-BIM Pipeline with Integrated Topology Refinement},
    author  = {Chamseddine, Mahdi and Kaufmann, Fabian and Schellen, Marius and Glock, Christian and Stricker, Didier and Rambach, Jason},
    journal = {arXiv preprint arXiv:2604.24311},
    year    = {2026}
}
```

---

## Acknowledgement

This research was funded by the European Union as part of the projects: HumanTech (Grant Agreement 101058236) and ShieldBOT (Grant Agreement 101235093).