Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: artistic-2.0
+tags:
+- visual-grounding
+- lidar
+- 3d
+---
+
+# 3EED: Ground Everything Everywhere in 3D — Dataset Card
+
+A cross-platform, multi-modal 3D visual grounding dataset spanning **vehicle**, **drone**, and **quadruped** platforms, with synchronized **RGB**, **LiDAR**, and **language** annotations. This page documents how to obtain and organize the dataset from HuggingFace and how to connect it with the training/evaluation code in the 3EED repository.
+
+- Project Page: https://3eed.github.io
+- Code (Baselines & Evaluation): https://github.com/iris0329/3eed
+- Paper: https://arxiv.org/ (coming soon)
+
+## 1. What’s Included
+
+- Platforms: `vehicle`, `drone`, `quad` (quadruped)
+- Modalities: LiDAR point clouds, RGB images, language referring expressions, metadata
+- Splits: train/val files per platform under `splits/`
+- Task: 3D visual grounding (language → 3D box)
+
+## 2. Download
+
+You can download via:
+- HuggingFace CLI:
+  ```bash
+  pip install -U "huggingface_hub[cli]"
+  huggingface-cli download 3EED/3EED --repo-type dataset --local-dir ./3eed_dataset
+  ````
+
+- Python:
+
+  ```python
+  from huggingface_hub import snapshot_download
+  snapshot_download(repo_id="3EED/3EED", repo_type="dataset", local_dir="./3eed_dataset")
+  ```
+- Git (LFS):
+
+  ```bash
+  git lfs install
+  git clone https://huggingface.co/datasets/3EED/3EED 3eed_dataset
+  ```
+
+## 3. Directory Structure
+
+- Place or verify the files under `data/3eed/` in your project. A minimal expected layout (paths shown relative to the repo root):
+  
+  ```
+  data/3eed/
+  ├── drone/                     # Drone platform data
+  │   ├── scene-0001/
+  │   │   ├── 0000_0/
+  │   │   │   ├── image.jpg
+  │   │   │   ├── lidar.bin
+  │   │   │   └── meta_info.json
+  │   │   └── ...
+  │   └── ...
+  ├── quad/                      # Quadruped platform data
+  │   ├── scene-0001/
+  │   └── ...
+  ├── waymo/                     # Vehicle platform data
+  │   ├── scene-0001/
+  │   └── ...
+  └── splits/                    # Train/val split files
+      ├── drone_train.txt
+      ├── drone_val.txt
+      ├── quad_train.txt
+      ├── quad_val.txt
+      ├── waymo_train.txt
+      └── waymo_val.txt
+  ```
+
+
+## 4. Connect to the Codebase
+
+- Clone the code repository:
+  
+  ```bash
+  git clone https://github.com/iris0329/3eed
+  cd 3eed
+  ```
+
+- Link or copy the downloaded dataset to `data/3eed/`:
+  
+  ```bash
+  # Example: if your dataset is in ../3eed_dataset
+  ln -s ../3eed_dataset data/3eed
+  ```
+
+Now you can follow the **Installation**, **Custom CUDA Operators**, **Training**, and **Evaluation** sections in the GitHub README:
+
+* Train on all platforms:
+
+  ```bash
+  bash scripts/train_3eed.sh
+  ```
+* Train on a single platform:
+
+  ```bash
+  bash scripts/train_waymo.sh   # vehicle
+  bash scripts/train_drone.sh   # drone
+  bash scripts/train_quad.sh    # quadruped
+  ```
+* Evaluate:
+
+  ```bash
+  bash scripts/val_3eed.sh
+  bash scripts/val_waymo.sh
+  bash scripts/val_drone.sh
+  bash scripts/val_quad.sh
+  ```
+
+Remember to set the correct `--checkpoint_path` inside the evaluation scripts.
+
+## 5. Data Splits
+
+We provide official splits under `data/3eed/splits/`:
+
+* `*_train.txt`: training scene/frame indices for each platform
+* `*_val.txt`: validation scene/frame indices for each platform
+
+Please keep these files unchanged for fair comparison with the baselines and reported results.
+
+## 6. Usage Tips
+
+* Storage: LiDAR+RGB data can be large; ensure sufficient disk space and use Git LFS for partial sync if needed.
+* IO Throughput: For faster training/evaluation, place frequently used scenes on fast local SSDs or use caching.
+* Reproducibility: Use the exact environment files and scripts from the code repo; platform unions vs. single-platform runs are controlled by the provided scripts.
+
+## 7. License
+
+* Dataset license: **Apache-2.0** (see the header of this page).
+* The **code repository** uses **Apache-2.0**; refer to the LICENSE in the GitHub repo.
+
+If you plan to use, redistribute, or modify the dataset, please review the dataset license and any upstream source licenses (e.g., Waymo Open Dataset, M3ED).
+
+## 8. Citation
+
+- If you find 3EED helpful, please cite:
+  ```bibtex
+  @inproceedings{li2025_3eed,
+    title     = {3EED: Ground Everything Everywhere in 3D},
+    author    = {Rong Li and Yuhao Dong and Tianshuai Hu and Ao Liang and 
+                 Youquan Liu and Dongyue Lu and Liang Pan and Lingdong Kong and 
+                 Junwei Liang and Ziwei Liu},
+    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)
+                 Datasets and Benchmarks Track},
+    year      = {2025}
+  }
+  ```
+
+## 9. Acknowledgements
+
+We acknowledge the following upstream sources which make this dataset possible:
+
+* Waymo Open Dataset (vehicle platform)
+* M3ED (drone and quadruped platforms)
+
+For baseline implementations and evaluation code, please refer to the GitHub repository.