3D-MOOD: Lifting 2D to 3D for Monocular Open-Set Object Detection
Yung-Hsu Yang, Luigi Piccinelli, Mattia Segu, Siyuan Li, Rui Huang, Yuqian Fu, Marc Pollefeys, Hermann Blum, Zuria Bauer
ICCV 2025, Paper at arXiv 2507.23567
-
27.08.2025: Addscripts/demo.pyand Huggingface Demo! -
25.08.2025: Release code and models. -
25.06.2025: 3D-MOOD is accepted at ICCV 2025!
We use Vis4D as the framework to implement 3D-MOOD. Please check the document for more details.
We support Python 3.11+ and PyTorch 2.4.0+. Please install the correct PyTorch version according to your own hardware settings.
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 .We provide the demo.py to test whether the installation is complete.
python scripts/demo.pyIt will save the prediction as follows to assets/demo/output.png.
You can also try the live demo on here!
We provide the HDF5 files and annotations here for ScanNet v2, Argoverse 2, and the depth GT for Omni3D datasets.
For training and testing with OmniD, please refer to DATA to setup the Omni3D data.
We also illustrate the coordinate system we use here.
The final data folder 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.hdf5By default, we use HDF5 as the data backend in our provided config.
You can convert each folder using the script to generate them.
It is worth noting that if you download the provided .hdf5 from here, you only need to convert each omni3d dataset to HDF5.
To be more specific:
cd data
python -m vis4d.data.io.to_hdf5 -p KITTI_object
python -m vis4d.data.io.to_hdf5 -p KITTI_object_depth # Only needed if you generate depth on your own
...
python -m vis4d.data.io.to_hdf5 -p hypersim
python -m vis4d.data.io.to_hdf5 -p hypersim_depth # Only needed if you generate depth on your ownThen you will have all datasets in .hdf5.
The other solution is to change the data_backend in the configs to FileBackend.
Note that the score of Argoverse 2 and ScanNet is the proposed open detection score (ODS), and the score forthe Omni3D test set is AP.
| Backbone | Config | Omni3D | Argoverse 2 | ScanNet |
|---|---|---|---|---|
| Swin-T | config | 28.4 | 22.4 | 30.2 |
| Swin-B | config | 30.0 | 23.8 | 31.5 |
For per-dataset results for Omni3D, please refer to Table 3 of the paper.
# Swin-T
vis4d test --config opendet3d/zoo/gdino3d/gdino3d_swin_t_omni3d.py --gpus 1 --ckpt https://huggingface.co/RoyYang0714/3D-MOOD/resolve/main/gdino3d_swin-t_120e_omni3d_699f69.pt
# Swin-B
vis4d test --config opendet3d/zoo/gdino3d/gdino3d_swin_b_omni3d.py --gpus 1 --ckpt https://huggingface.co/RoyYang0714/3D-MOOD/resolve/main/gdino3d_swin-b_120e_omni3d_834c97.ptWe use a batch size of 128 to train our models.
The setting is assumed to be running on the cluster using RTX 4090.
# Swin-T
vis4d fit --config opendet3d/zoo/gdino3d/gdino3d_swin_t_omni3d.py --gpus 8 --nodes 4 --config.params.samples_per_gpu=4
# Swin-B
vis4d fit --config opendet3d/zoo/gdino3d/gdino3d_swin_b_omni3d.py --gpus 8 --nodes 8We also provide the code to reproduce our ScanNet200 results in the supplementary. Note that it will take a longer time since we need to chunk the classes.
vis4d test --config opendet3d/zoo/gdino3d/gdino3d_swin_b_scannet200.py --gpus 1 --ckpt https://huggingface.co/RoyYang0714/3D-MOOD/resolve/main/gdino3d_swin-b_120e_omni3d_834c97.ptIt will dump all the visualization results under vis4d-workspace/gdino3d_swin-b_omni3d/${VERSION}/vis/test/.
vis4d test --config opendet3d/zoo/gdino3d/gdino3d_swin_b_omni3d.py --gpus 1 --ckpt https://huggingface.co/RoyYang0714/3D-MOOD/resolve/main/gdino3d_swin-b_120e_omni3d_834c97.pt --vis --config.params.nms=True --config.params.score_threshold=0.1If you find our work useful in your research, please consider citing our publications:
@InProceedings{Yang_2025_ICCV,
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},
title = {3D-MOOD: Lifting 2D to 3D for Monocular Open-Set Object Detection},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {7429-7439}
}