Improve dataset card: Add task category, tags, paper/code links, and sample usage

This PR enhances the `InfraDepth` dataset card by:
- Adding the `task_categories` metadata (`depth-estimation`).
- Including relevant `tags` such as `3d-point-cloud`, `image-restoration`, `image-segmentation`, and `civil-engineering` for improved discoverability.
- Providing a direct link to the paper ([https://huggingface.co/papers/2509.03324](https://huggingface.co/papers/2509.03324)).
- Adding a link to the official GitHub repository for the associated code ([https://github.com/Jingyixiong/InfraDiffusion-official-implement](https://github.com/Jingyixiong/InfraDiffusion-official-implement)).
- Incorporating a "Sample Usage" section with code snippets from the GitHub README, demonstrating how to run InfraDiffusion using the dataset.
- Populating the "Citation" section with the full BibTeX entry from the paper.

These changes will make the dataset more discoverable and easier to use for the community.

README.md

@@ -1,10 +1,17 @@
 ---
-license: cc-by-nc-4.0
 language:
 - en
-pretty_name: InfraDepth
+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
@@ -16,6 +23,11 @@ The dataset combines **3D point clouds of masonry bridges and tunnels**, project
 
 ---
 
+**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
@@ -74,6 +86,49 @@ Each `patch_{idx}.npz` file contains:
 
 ---
 
-## 📌 Citation
+## ✨ 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}
+}
+```