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Roydon728/
PolarAPP

Name: PolarAPP
Availability: InStock

pytorch

polarization

demosaicking

shape-from-polarization

reflection-removal

image-to-image

2603.23071

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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

text

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

python

from huggingface_hub import snapshot_download

checkpoint_root = snapshot_download("Roydon728/PolarAPP")

Pass the task subdirectory to the corresponding inference command:

bash

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

bibtex

@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}
}