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

This PR significantly improves the dataset card for ByteCameraDepth by:
- Adding `task_categories: - depth-estimation` to the metadata, making the dataset discoverable by its primary AI task.
- Including relevant `tags: - robotics`, `- 3d`, and `- simulation` to further enhance searchability, reflecting the dataset's context in robotic manipulation and 3D geometry perception, often used for bridging the sim-to-real gap.
- Adding prominent links to the associated paper ([Manipulation as in Simulation: Enabling Accurate Geometry Perception in Robots](https://huggingface.co/papers/2509.02530)), the project page ([https://manipulation-as-in-simulation.github.io/](https://manipulation-as-in-simulation.github.io/)), and the main GitHub repository ([https://github.com/ByteDance-Seed/manip-as-in-sim-suite](https://github.com/ByteDance-Seed/manip-as-in-sim-suite)) for easy access to related resources.
- Incorporating a "Sample Usage" section with a code snippet from the GitHub README, demonstrating how to run depth inference using the Camera Depth Models (CDMs) trained with this dataset.
- Updating the BibTeX citation to include the Hugging Face paper URL for better traceability.

These updates provide a more comprehensive and organized overview for users exploring the ByteCameraDepth dataset.

README.md

@@ -1,9 +1,17 @@
 ---
 license: cc-by-4.0
+task_categories:
+- depth-estimation
+tags:
+- robotics
+- 3d
+- simulation
 ---
 
 # ByteCameraDepth Dataset
 
+[Paper](https://huggingface.co/papers/2509.02530) | [Project Page](https://manipulation-as-in-simulation.github.io/) | [Code](https://github.com/ByteDance-Seed/manip-as-in-sim-suite)
+
 ByteCameraDepth is a multi-camera depth estimation dataset containing synchronized depth, color, and auxiliary data captured from various 3D cameras. The dataset provides comprehensive depth sensing from multiple cameras in various in-door scenarios, making it ideal for developing and evaluating depth estimation algorithms.
 
 ## Dataset Overview
@@ -26,6 +34,20 @@ cat recorded_data.tar.part.* | tar -xvf -
 
 This will create a `recorded_data` folder containing all 39 recording sessions.
 
+### Sample Usage
+
+To run depth inference on RGB-D camera data using the CDM (Camera Depth Models) trained with this dataset:
+
+```bash
+cd cdm
+python infer.py \
+    --encoder vitl \
+    --model-path /path/to/model.pth \
+    --rgb-image /path/to/rgb.jpg \
+    --depth-image /path/to/depth.png \
+    --output result.png
+```
+
 ## Dataset Structure
 
 ### Archive Organization
@@ -142,7 +164,8 @@ If you use this dataset in your research, please cite:
           Li, Xinyao and Chen, Jingxiao and Xu, Jiafeng and Yang, Yichu and Lin, Yunfeng and 
           Li, Xinghang and Yu, Yong and Zhang, Weinan and Kong, Tao and Kang, Bingyi},
   journal={arXiv preprint},
-  year={2025}
+  year={2025},
+  url={https://huggingface.co/papers/2509.02530}
 }
 ```