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license: cc-by-nc-4.0
library_name: pytorch
pipeline_tag: image-feature-extraction
tags:
- person-re-identification
- aerial-ground
- vision-transformer
- eccv2026
---
# 3D-LENS
Checkpoints of **3D-LENS: A 3D Lifting-based Elevated Novel-view Synthesis method for Single-View Aerial-Ground
Re-Identification** (ECCV 2026): [paper](https://link.springer.com/chapter/10.1007/978-3-032-36846-1_12),
[arXiv:2604.26520](https://arxiv.org/abs/2604.26520), [poster](https://eccv.ecva.net/virtual/2026/poster/5389).
Code, training and evaluation are at <https://github.com/TurtleSmoke/3D-LENS>.
## Checkpoints
Each checkpoint is trained on one camera type of AG-ReID (`agreid_*`) or AG-ReID.v2 (`agreid_v2_*`), given by its
name. The two AG-ReID.v2 aerial checkpoints are evaluated against CCTV and wearable images respectively.
| Checkpoint | Query → Gallery | mAP | Rank-1 |
|---------------------------------|-----------------|-------|--------|
| `agreid_aerial.pth` | A → G | 49.78 | 62.84 |
| | G → A | 49.44 | 57.59 |
| `agreid_ground.pth` | A → G | 53.26 | 65.17 |
| | G → A | 55.05 | 67.36 |
| `agreid_v2_aerial_cctv.pth` | C → A | 37.84 | 47.10 |
| | A → C | 42.77 | 55.69 |
| `agreid_v2_cctv.pth` | C → A | 47.21 | 58.53 |
| | A → C | 41.36 | 50.76 |
| `agreid_v2_aerial_wearable.pth` | W → A | 28.61 | 35.77 |
| | A → W | 34.26 | 47.13 |
| `agreid_v2_wearable.pth` | W → A | 38.27 | 46.24 |
| | A → W | 38.93 | 47.89 |
A = aerial, G = ground, C = CCTV, W = wearable. The checkpoints are stored in fp16, so they can differ slightly from
the paper.
## Usage
```bash
git clone https://github.com/TurtleSmoke/3D-LENS && cd 3D-LENS && uv sync && source .venv/bin/activate
hf download TurtleSmoke/3D-LENS --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 [AG-ReID.v2](https://github.com/huynguyen792/AG-ReID.v2)
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{grolleau2026lens,
title = {3D-LENS: A 3D Lifting-based Elevated Novel-view Synthesis method for Single-View Aerial-Ground Re-Identification},
author = {Grolleau, William and Sabourin, Astrid and Lapouge, Guillaume and Achard, Catherine},
booktitle = {European Conference on Computer Vision (ECCV)},
year = {2026}
}
```
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