| --- |
| library_name: pytorch |
| pipeline_tag: image-to-image |
| tags: |
| - polarization |
| - demosaicking |
| - shape-from-polarization |
| - reflection-removal |
| --- |
| |
| # PolarAPP checkpoints |
|
|
| This repository contains the released checkpoints for the two downstream tasks in [PolarAPP](https://arxiv.org/abs/2603.23071): |
|
|
| - `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](https://github.com/YouweiLyu/SfPUEL). Follow the dataset terms and citation guidance from the provider. |
| - DfP uses [PolaRGB](https://huggingface.co/datasets/Mingde/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} |
| } |
| ``` |
|
|
