File size: 2,197 Bytes
903f57e f963a6c b2159b9 903f57e f963a6c b2159b9 f963a6c b2159b9 f963a6c b7fa352 f963a6c b2159b9 f963a6c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 | ---
license: cc-by-nc-4.0
library_name: pytorch
pipeline_tag: image-feature-extraction
tags:
- person-re-identification
- aerial-ground
- vision-transformer
- neurips2026
---
# 3D-VITAL
Checkpoints of **3D-VITAL: Visibility-aware Identity Training from Artificial Liftings of 3D Reconstruction model for
Cross-View Object Re-Identification** (NeurIPS 2026): [arXiv:2610.16692](https://arxiv.org/abs/2610.16692),
[poster](https://neurips.cc/virtual/2026/loc/paris/poster/150244).
Code, training and evaluation are at <https://github.com/TurtleSmoke/3D-VITAL>.
## Checkpoints
Each checkpoint is trained on one camera type of AG-ReID, given by its name.
| Checkpoint | Query → Gallery | mAP | Rank-1 |
|---------------------|-----------------|-------|--------|
| `agreid_aerial.pth` | A → G | 52.09 | 66.20 |
| | G → A | 53.30 | 62.68 |
| `agreid_ground.pth` | A → G | 55.99 | 67.13 |
| | G → A | 57.93 | 68.61 |
A = aerial, G = ground. The checkpoints are stored in fp16, so they can differ slightly from the paper.
## Usage
```bash
git clone https://github.com/TurtleSmoke/3D-VITAL && cd 3D-VITAL && uv sync && source .venv/bin/activate
hf download TurtleSmoke/3D-VITAL --include "*.pth" --local-dir checkpoints
bash scripts/eval.sh agreid_aerial $DATA checkpoints/agreid_aerial.pth
```
## License
The weights are released for non-commercial research under CC BY-NC 4.0. They were trained on
[AG-ReID](https://github.com/huynguyen792/AG-ReID) and on synthetic views generated with
[Hunyuan3D-2](https://github.com/Tencent/Hunyuan3D-2) and [StyleID](https://github.com/jiwoogit/StyleID), whose
respective terms also apply. The code is under the CeCILL-B license.
## Citation
```bibtex
@inproceedings{grolleau2026vital,
title = {3D-VITAL: Visibility-aware Identity Training from Artificial Liftings of 3D Reconstruction model for Cross-View Object Re-Identification},
author = {Grolleau, William and Sabourin, Astrid and Lapouge, Guillaume and Achard, Catherine},
booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
year = {2026}
}
```
|