Enhance dataset card: Add task category, links, detailed info, and usage

README.md

@@ -1,13 +1,103 @@
 ---
 language:
 - en
+task_categories:
+- image-to-3d
+tags:
+- 3d-object-detection
+- monocular
+- open-set
 ---
-# 3D-MOOD
 
-<!-- Provide a quick summary of the dataset. -->
+# 3D-MOOD Dataset
 
-This dataset is for [3D-MOOD](https://arxiv.org/abs/2507.23567).
+<div align="center">
+  <img src="https://github.com/cvg/3D-MOOD/blob/main/assets/overview.png" width="100%" alt="3D-MOOD Overview" align="center">
+</div>
 
-It contains the selected images and annotations from [Argoverse 2](https://www.argoverse.org/av2.html) and [ScanNetV2](http://www.scan-net.org/), and also the depth GT for [Omni3D](https://github.com/facebookresearch/omni3d/blob/main/DATA.md) data.
+This dataset is for [3D-MOOD: Lifting 2D to 3D for Monocular Open-Set Object Detection](https://arxiv.org/abs/2507.23567). It contains selected images and annotations from [Argoverse 2](https://www.argoverse.org/av2.html) and [ScanNetV2](http://www.scan-net.org/), and also the depth ground truth (GT) for [Omni3D](https://github.com/facebookresearch/omni3d/blob/main/DATA.md) data. We provide the HDF5 data and annotation in JSON format.
 
-We provide the HDF5 data and annotation in JSON format.
+*   **Paper:** [3D-MOOD: Lifting 2D to 3D for Monocular Open-Set Object Detection](https://arxiv.org/abs/2507.23567)
+*   **Project Page:** [https://royyang0714.github.io/3D-MOOD](https://royyang0714.github.io/3D-MOOD)
+*   **Code:** [https://github.com/cvg/3D-MOOD](https://github.com/cvg/3D-MOOD)
+
+## Introduction
+
+Monocular 3D object detection is valuable for various applications such as robotics and AR/VR. This dataset is associated with 3D-MOOD, the first end-to-end 3D Monocular Open-set Object Detector, which addresses monocular 3D object detection in an open-set setting. The approach involves lifting open-set 2D detection into 3D space, enabling end-to-end joint training for both 2D and 3D tasks to yield better overall performance.
+
+## Data Preparation
+
+The HDF5 files and annotations for ScanNet v2, Argoverse 2, and the depth GT for Omni3D datasets are provided. For training and testing with Omni3D, please refer to the [DATA guide](https://github.com/cvg/3D-MOOD/blob/main/docs/DATA.md) in the GitHub repository to set up the Omni3D data.
+
+The final data folder structure should be like:
+
+```
+REPO_ROOT
+├── data
+│   ├── omni3d
+│   │   └── annotations
+├── KITTI_object
+├── KITTI_object_depth
+├── nuscenes
+├── nuscenes_depth
+├── objectron
+├── objectron_depth
+├── SUNRGBD
+├── ARKitScenes
+├── ARKitScenes_depth
+├── hypersim
+├── hypersim_depth
+├── argoverse2
+│   ├── annotations
+│   └── val.hdf5
+└── scannet
+    ├── annotations
+    └── val.hdf5
+```
+
+By default, in our provided config, we use `HDF5` as the data backend. You can convert each folder using the [script](https://github.com/SysCV/vis4d/blob/main/vis4d/data/io/to_hdf5.py) to generate them, or you can just change the `data_backend` in the configs to `FileBackend`.
+
+## Sample Usage
+
+We provide the [`demo.py`](https://github.com/cvg/3D-MOOD/blob/main/scripts/demo.py) to test whether the installation is complete.
+
+First, install the necessary packages (for full installation instructions, refer to the [GitHub repository](https://github.com/cvg/3D-MOOD#installation)):
+
+```bash
+conda create -n opendet3d python=3.11 -y
+
+conda activate opendet3d
+
+# Install Vis4D
+# It should also install the PyTorch with CUDA support. But please check.
+pip install vis4d==1.0.0
+
+# Install CUDA ops
+pip install git+https://github.com/SysCV/vis4d_cuda_ops.git --no-build-isolation --no-cache-dir
+
+# Install 3D-MOOD
+pip install -v -e .
+```
+
+Then, run the demo script:
+
+```bash
+python scripts/demo.py
+```
+
+It will save the prediction as follows to `assets/demo/output.png`.
+
+You can also try the live demo on [Hugging Face Spaces](https://huggingface.co/spaces/RoyYang0714/3D-MOOD)!
+
+## Citation
+
+If you find our work useful in your research please consider citing our publications:
+
+```bibtex
+@article{yang20253d,
+  title={3D-MOOD: Lifting 2D to 3D for Monocular Open-Set Object Detection},
+  author={Yang, Yung-Hsu and Piccinelli, Luigi and Segu, Mattia and Li, Siyuan and Huang, Rui and Fu, Yuqian and Pollefeys, Marc and Blum, Hermann and Bauer, Zuria},
+  journal={arXiv preprint arXiv:2507.23567},
+  year={2025}
+}
+```