PolarAPP checkpoints
This repository contains the released checkpoints for the two downstream tasks in PolarAPP:
SfP/: full-resolution shape from polarization with a TaskNet designed specifically for PolarAPP.DfP/: full-resolution de-reflection from polarization with a PolarFree-based TaskNet.
Files
SfP/
|-- DemNet/DemNet.pth
|-- TaskNet/TaskNet.pth
`-- FANet/FANet.pth
DfP/
|-- DemNet/DemNet.pth
|-- TaskNet/TaskNet.pth
`-- FANet/FANet.pth
DemNet reconstructs full-resolution polarization observations. TaskNet performs the downstream task. FANet supports feature alignment during training.
Download
from huggingface_hub import snapshot_download
checkpoint_root = snapshot_download("Roydon728/PolarAPP")
Pass the task subdirectory to the corresponding inference command:
cd SfP
python infer.py --input-dir ./Datasets/Testsets --ckpt-dir <snapshot>/SfP
cd DfP
python infer.py --input-dir ./Datasets/PolaRGB --ckpt-dir <snapshot>/DfP \
--polarfree-checkpoint-dir ./experiments/checkpoints/polarfree
The DfP workflow loads the PolarFree diffusion-prior files separately using their upstream filenames.
Data
- SfP dataset links are distributed through SfPUEL. Follow the dataset terms and citation guidance from the provider.
- DfP uses PolaRGB, distributed under CC BY-NC 4.0.
Integrity
SHA-256 checksums are provided in SHA256SUMS.txt.
Citation
If you use these model weights in your research, please cite:
@inproceedings{luo2026polarapp,
title = {PolarAPP: Beyond Polarization Demosaicking for Polarimetric Applications},
author = {Luo, Yidong and Li, Chenggong and Song, Yunfeng and Wang, Ping and Shi, Boxin and Zhang, Junchao and Yuan, Xin},
booktitle = {European Conference on Computer Vision (ECCV)},
series = {Lecture Notes in Computer Science},
volume = {17024},
pages = {115--133},
publisher = {Springer},
address = {Cham},
year = {2026},
doi = {10.1007/978-3-032-37556-8_7}
}