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

@article{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},
  journal = {arXiv preprint arXiv:2603.23071},
  year    = {2026}
}