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, arXiv:2604.26520, poster. 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
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 and AG-ReID.v2 and on synthetic views generated with Hunyuan3D-2 and StyleID, whose respective terms also apply. The code is under the CeCILL-B license.
Citation
@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}
}