Datasets:
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| pretty_name: PolarFree | |
| tags: | |
| - computer-vision | |
| - reflection-removal | |
| - polarization | |
| - image-processing | |
| - cvpr2025 | |
| license: cc-by-nc-4.0 | |
| datasets: | |
| - polarfree | |
| language: | |
| - en | |
| size_categories: | |
| - 10K<n<100K | |
| task_categories: | |
| - image-to-image | |
| # PolarFree: Polarization-based Reflection-Free Imaging | |
| ## Dataset Overview | |
| PolarFree is a high-quality dataset designed for polarization-based reflection removal tasks, as introduced in the CVPR 2025 paper "PolarFree: Polarization-based Reflection-Free Imaging". The dataset aims to support tasks such as image reflection removal and image enhancement, particularly suitable for training and evaluating polarization-based image reflection removal models. | |
| ## Download Dataset | |
| ``` | |
| huggingface-cli download Mingde/PolaRGB --repo-type dataset --local-dir ./PolaRGB | |
| ``` | |
| ## Dataset Structure | |
| The dataset is organized as follows: | |
| ``` | |
| dataset/ | |
| ├── train/ | |
| │ ├── easy (or hard)/ # difficulty split | |
| │ │ ├── input/ | |
| │ │ │ ├── 00/ # scene 0 | |
| │ │ │ │ ├── 000_000.png | |
| │ │ │ │ ├── 000_045.png | |
| │ │ │ │ ├── 000_090.png | |
| │ │ │ │ ├── 000_135.png | |
| │ │ │ │ ├── 000_rgb.png | |
| │ │ │ │ ├── 001_000.png | |
| │ │ │ │ ├── 001_045.png | |
| │ │ │ │ ├── 001_090.png | |
| │ │ │ │ ├── 001_135.png | |
| │ │ │ │ ├── 001_rgb.png | |
| │ │ │ │ └── ... | |
| │ │ │ ├── 01/ # scene 1 | |
| │ │ │ │ ├── 000_000.png | |
| │ │ │ │ ├── 000_045.png | |
| │ │ │ │ ├── 000_090.png | |
| │ │ │ │ ├── 000_135.png | |
| │ │ │ │ ├── 000_rgb.png | |
| │ │ │ │ ├── 001_000.png | |
| │ │ │ │ ├── 001_045.png | |
| │ │ │ │ ├── 001_090.png | |
| │ │ │ │ ├── 001_135.png | |
| │ │ │ │ ├── 001_rgb.png | |
| │ │ │ │ └── ... | |
| │ │ │ └── ... | |
| │ │ ├── gt/ | |
| │ │ │ ├── 00/ # scene 0 | |
| │ │ │ │ ├── 000_000.png | |
| │ │ │ │ ├── 000_045.png | |
| │ │ │ │ ├── 000_090.png | |
| │ │ │ │ ├── 000_135.png | |
| │ │ │ │ └── 000_rgb.png | |
| │ │ │ ├── 01/ # scene 1 | |
| │ │ │ │ ├── 000_000.png | |
| │ │ │ │ ├── 000_045.png | |
| │ │ │ │ ├── 000_090.png | |
| │ │ │ │ ├── 000_135.png | |
| │ │ │ │ └── 000_rgb.png | |
| │ │ │ └── ... | |
| │ | |
| ├── test/ | |
| │ ├── input/ | |
| │ │ ├── 00/ # scene 0 | |
| │ │ │ ├── 000_000.png | |
| │ │ │ ├── 000_045.png | |
| │ │ │ ├── 000_090.png | |
| │ │ │ ├── 000_135.png | |
| │ │ │ ├── 000_rgb.png | |
| │ │ │ └── ... | |
| │ │ ├── 01/ # scene 1 | |
| │ │ │ ├── 000_000.png | |
| │ │ │ ├── 000_045.png | |
| │ │ │ ├── 000_090.png | |
| │ │ │ ├── 000_135.png | |
| │ │ │ ├── 000_rgb.png | |
| │ │ │ └── ... | |
| │ │ └── ... | |
| │ | |
| │ ├── gt/ | |
| │ │ ├── 00/ # scene 0 | |
| │ │ │ ├── 000_000.png | |
| │ │ │ ├── 000_045.png | |
| │ │ │ ├── 000_090.png | |
| │ │ │ ├── 000_135.png | |
| │ │ │ └── 000_rgb.png | |
| │ │ ├── 01/ # scene 1 | |
| │ │ │ ├── 000_000.png | |
| │ │ │ ├── 000_045.png | |
| │ │ │ ├── 000_090.png | |
| │ │ │ ├── 000_135.png | |
| │ │ │ └── 000_rgb.png | |
| │ │ └── ... | |
| └── ... | |
| ``` | |
| 1. The dataset is divided into **train** and **test** subsets, and both follow a similar directory structure. | |
| 2. The **train** subset is further divided into **easy** and **hard** sets, which share the same internal structure. The **test** subset is not split by difficulty, but its structure is identical. | |
| 3. Taking the **test** set as an example, it contains two subfolders: **input** and **gt**. The *input* folder stores images with reflections, while the *gt* folder contains the corresponding clean images without reflections. Both folders include the same number of scenes (e.g., 00, 01, 02, ...). | |
| 4. In `test/input/00`, there are multiple images named in the format `xxx_yyy.png`. Here, **xxx** denotes the index of the captured sample within scene `00`. **yyy** may be one of {000, 045, 090, 135, rgb}: the first four represent polarization-based images, and `rgb` corresponds to the RGB image of the current scene. | |
| 5. In `test/gt/00`, there is a single image named `000_yyy.png`, where '000_rgb.png' serves as the ground truth for all captured samples in the corresponding `input/00` folder. | |
| To train or evaluate a polarization-based reflection removal model, each sample pair can be constructed as follows. For a given scene (e.g., 00), the input consists of five images—000_000.png, 000_045.png, 000_090.png, 000_135.png, and 000_rgb.png—and the corresponding ground truth is gt/00/000_rgb.png. Similarly: | |
| - input/00/{001_000.png, 001_045.png, 001_090.png, 001_135.png, 001_rgb.png} → gt/00/000_rgb.png | |
| - input/00/{002_000.png, 002_045.png, 002_090.png, 002_135.png, 002_rgb.png} → gt/00/000_rgb.png | |
| and so on for the remaining examples within the same scene. | |
| If you want raw images, please find them at https://huggingface.co/datasets/Mingde/PolaRGB_raw or contact me via mingdeyao@foxmail.com. | |
| ## Citation | |
| If you use the PolarFree dataset in your research, please cite the following paper: | |
| ```bibtex | |
| @inproceedings{polarfree2025, | |
| title={PolarFree: Polarization-based Reflection-Free Imaging}, | |
| author={Yao, Mingde and Wang, Menglu and Tam, King-Man and Li, Lingen and Xue, Tianfan and Gu, Jinwei}, | |
| booktitle={CVPR}, | |
| year={2025} | |
| } | |
| ``` | |
| For more information, please visit the project page: | |
| - GitHub Repository: [https://github.com/mdyao/PolarFree](https://github.com/mdyao/PolarFree) | |
| - Paper: [https://arxiv.org/abs/2503.18055](https://arxiv.org/abs/2503.18055) | |
| --- |