| --- |
| license: other |
| license_name: nuscenes-terms-of-use |
| license_link: https://www.nuscenes.org/terms-of-use |
| tags: |
| - occupancy-prediction |
| - open-vocabulary |
| - autonomous-driving |
| - nuscenes |
| --- |
| |
| # AGO: Adaptive Grounding for Open World 3D Occupancy Prediction |
|
|
| Pretrained weights for [AGO](https://github.com/EdwardLeeLPZ/AGO) (ICCV 2025) on Occ3D-nuScenes. Paper: [arXiv:2504.10117](https://arxiv.org/abs/2504.10117). |
|
|
| ## Weights |
|
|
| Each file contains the model `state_dict`. The CLIP weights are not included and are loaded from FeatUp at runtime. |
|
|
| | File | Config | Known mIoU / Unknown mIoU / mIoU | |
| | :--- | :--- | :-: | |
| | `ago_nuscenes_self_supervised.pth` | `ago_nuscenes_self_supervised.py` | - / - / 19.16 | |
| | `ago_nuscenes_open_world_pretraining.pth` | `ago_nuscenes_open_world_pretraining.py` | 23.23 / 0.00 / 11.61 | |
| | `ago_nuscenes_open_world_pretraining.pth` (zero-shot) | `ago_nuscenes_open_world_pretraining.py` | 33.95 / 0.42 / 7.13 | |
| | `ago_nuscenes_open_world_finetuning.pth` | `ago_nuscenes_open_world_finetuning.py` | 36.88 / 8.78 / 14.40 | |
|
|
| ## Usage |
|
|
| ```shell |
| hf download EdwardLeeLPZ/AGO --include "*.pth" --local-dir checkpoints |
| bash tools/dist_test.sh projects/configs/ago_nuscenes_self_supervised.py checkpoints/ago_nuscenes_self_supervised.pth 8 --tag self_supervised |
| ``` |
|
|
| See the [code repository](https://github.com/EdwardLeeLPZ/AGO) for installation and data preparation. |
|
|
| ## License |
|
|
| The weights are trained on nuScenes and subject to the [nuScenes terms of use](https://www.nuscenes.org/terms-of-use). |
|
|
| ## Citation |
|
|
| ``` |
| @inproceedings{li2025ago, |
| title={AGO: Adaptive Grounding for Open World 3D Occupancy Prediction}, |
| author={Li, Peizheng and Ding, Shuxiao and Zhou, You and Zhang, Qingwen and Inak, Onat and Triess, Larissa and Hanselmann, Niklas and Cordts, Marius and Zell, Andreas}, |
| booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, |
| pages={8645--8655}, |
| year={2025} |
| } |
| ``` |
|
|
