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---
language:
- en
license: cc-by-nc-4.0
size_categories:
- 100M<n<1B
pretty_name: InfraDepth
task_categories:
- depth-estimation
tags:
- 3d-point-cloud
- image-restoration
- image-segmentation
- civil-engineering
---
## InfraDepth
`InfraDepth` is a multimodal dataset of rendered depth map patches for masonry bridges and tunnels.
It is designed to support research on **image restoration, inpainting, sparse-to-dense depth reconstruction, and segmentation** of civil infrastructure components.
The dataset combines **3D point clouds of masonry bridges and tunnels**, projected through a virtual camera into patches, then stored as `.npz` files with depth maps, masks, and camera parameters.
---
**Paper**: [InfraDiffusion: zero-shot depth map restoration with diffusion models and prompted segmentation from sparse infrastructure point clouds](https://huggingface.co/papers/2509.03324)
**Code**: [https://github.com/Jingyixiong/InfraDiffusion-official-implement](https://github.com/Jingyixiong/InfraDiffusion-official-implement)
---
## 📁 Dataset Structure
```bash
datasets/
│
├── masonry_bridges/
│ ├── begc/
│ │ ├── arch/
│ │ │ ├── 0/
│ │ │ │ └── rendered_0.8_0.8_0.5/
│ │ │ │ ├── patch_0.npz
│ │ │ │ ├── patch_0_cam_params.npz
│ │ │ │ └── ...
│ │ ├── pier/
│ │ └── spandrel_wall/
│ │
│ └── hertfordshire/
│ ├── arch/
│ ├── pier/
│ └── spandrel_wall/
│
└── tunnels/
└── wheatly_tunnel/
├── S-15/
│ ├── arch/
│ └── pier/
├── S-20/
│ ├── arch/
│ └── pier/
└── S-25/
├── arch/
└── pier/
```
Each component folder (for example, `arch/0/`) contains a folder named `rendered_0.8_0.8_0.5/` where the patches are stored.
The suffix `0.8_0.8_0.5` indicates the patch bounding box size in meters (x, y, z).
---
## 🔹 File Formats
Inside each `rendered_0.8_0.8_0.5/` folder:
```bash
| File name | Format | Description |
|-----------------------------|--------|-------------|
| patch_{idx}.npz | NPZ | Contains depth map and masks |
| patch_{idx}_cam_params.npz | NPZ | Camera intrinsics and extrinsics for the patch |
Each `patch_{idx}.npz` file contains:
- `depth_map`: Rendered depth values(original depth map without image restoration)
- `mask_inpainting`: Mask region for inpainting
- `mask_boundary`: Boundary mask of the patch
```
---
## ✨ Sample Usage
The `InfraDepth` dataset is designed to be used with the `InfraDiffusion` framework. Below are examples from the official GitHub repository on how to run InfraDiffusion restoration using the dataset:
**(1) Masonry Tunnel Dataset**
```bash
python main.py data=tunnels \
image_restore.deg=inpainting \
image_restore.sigma_y=0.16 \
general.save_results=true
```
**(2) Masonry Bridge Dataset**
```bash
python main.py data=masonry_bridges \
image_restore.deg=inpainting \
image_restore.sigma_y=0.16 \
general.save_results=true
```
**(3) Selecting a Specific Infrastructure (infrastructure names can be found in `configs/data`)**
Example: To just get image restoration results on `hertfordshire`, override it:
```bash
python main.py \
data=masonry_bridges \
data.infra_name='begc' \
image_restore.deg=inpainting \
image_restore.sigma_y=0.16 \
general.save_results=true
```
For more detailed usage instructions, including environment setup and SAM segmentation, please refer to the [official GitHub repository](https://github.com/Jingyixiong/InfraDiffusion-official-implement).
---
## 📚 Citation
If you use this dataset, please cite the associated paper:
```bibtex
@article{jing2025infradiffusion,
title={InfraDiffusion: zero-shot depth map restoration with diffusion models and prompted segmentation from sparse infrastructure point clouds},
author={Jing, Yixiong and Zhang, Cheng and Wu, Haibing and Wang, Guangming and Wysocki, Olaf and Sheil, Brian},
year={2025},
note={Preprint}
}
``` |