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spectro-polarimetric

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Spectro-polarimetric Dataset

We provide a spectro-polarimetric dataset. This dataset consists of full-Stokes images for both hyperspectral and trichromatic scenes. Hyperspectral dataset has 311 scenes and trichromatic dataset has 2022 scenes.

For more details, see our paper on Spectral and Polarization Vision: Spectro-polarimetric Real-world Dataset.

⚠️ Notice: Known Issues in Hyperspectral Dataset

1. Measurement Errors
Specific wavelengths and polarization components were not measured correctly.
IDs: 089, 112

2. Polarization Misalignment
Misalignment exists between x-polarization and y-polarization channels.
IDs: 001, 029, 032, 047, 052, 059, 060, 061, 062, 201, 205, 269, 294, 307, 308

3. Misalignment due to Scene Motion
Slight misalignment occurred due to object movement within the scene during capture.
IDs: 223, 229, 285, 292, 293, 295, 306, 309, 310

💾 File Hierarchy

📂 trichromatic/: Trichromatic polarimetric dataset
- 📂 original/: Data processed from captured raw files
  - 📄 0000.npy ~
- 📂 denoised/: Data processed from denoised raw files
  - 📄 0000.npy ~
- 📂 mask/: Masks for the scenes
  - 📄 0000.png ~
- 📄 labeling_trichromatic.csv: Labeling each scene
📂 hyperspectral/: Hyperspectral polarimetric dataset
- 📂 original/: Data processed from captured raw files
  - 📄 0000.npy ~
- 📂 denoised/: Data processed from denoised raw files
  - 📄 0000.npy ~
- 📂 mask/: Mask fors the scenes
  - 📄 0000.png ~
- 📄 labeling_hyperspectral.csv: Labeling each scene
📄 README.md

📷 Trichromatic Data Overview

original & denoised:
- Format: Stokes numpy files (1900, 2100, 4, 3)
- Dimensions:
  - R: Spatial dimension
  - C: Spatial dimension
  - s: Stokes axis (s0, s1, s2, s3)
  - c: Spectral axis (R G B)
mask:
- Binary mask images (1900, 2100)
- Dimensions:
  - R: Spatial dimension
  - C: Spatial dimension

📷 Hyperspectral Data Overview

original & denoised:
- Format: Stokes numpy files (512, 612, 4, 21)
- Dimensions:
  - R: Spatial dimension
  - C: Spatial dimension
  - s: Stokes axis (s0, s1, s2, s3)
  - c: Spectral axis (450nm ~ 650nm at 10nm intervals)
mask:
- Binary mask images (512, 612)
- Dimensions:
  - R: Spatial dimension
  - C: Spatial dimension

🗂️ Labeling Information

SceneNum: Indices of scenes (0 ~ 310 for hyperspectral, 0 ~ 2021 for trichromatic)
Content: Type of content (scene, object)
Timezone: Scene environment (indoor, day, night)
Illumination: Illumination condition (white, yellow, sunny, cloudy)

📜 Citation

@InProceedings{Jeon_2024_CVPR,
    author    = {Jeon, Yujin and Choi, Eunsue and Kim, Youngchan and Moon, Yunseong and Omer, Khalid and Heide, Felix and Baek, Seung-Hwan},
    title     = {Spectral and Polarization Vision: Spectro-polarimetric Real-world Dataset},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2024},
    pages     = {22098-22108}
}

📫 Contact

If you have any questions, please contact

Yujin Jeon: jyj7913@postech.ac.kr
Eunsue Choi: ches7283@postech.ac.kr
Seung-hwan Baek: shwbaek@postech.ac.kr

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