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README.md
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---
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license: cc-by-4.0
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---
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license: cc-by-4.0
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task_categories:
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- image-to-image
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tags:
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- relighting
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- HDR
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- environment-map
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- outdoor
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- real-world
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pretty_name: WildRelight
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---
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# WildRelight Dataset
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A real-world relighting dataset of 30 outdoor/semi-indoor scenes captured across multiple time steps throughout the day. Each scene provides paired HDR photographs and HDR environment maps, enabling evaluation of outdoor relighting tasks.
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## Key Statistics
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| Property | Value |
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|----------|-------|
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| Scenes | 30 |
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| Time steps per scene | 6–8 (spaced ~1 hour apart) |
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| Total HDR photo/envmap pairs | 196 |
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| Photo resolution (full) | 6048 × 4024 px |
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| Envmap resolution (full) | 7680 × 3840 px (equirectangular) |
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| Photo resolution (small) | 1200 × ~798 px |
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| Envmap resolution (small) | 1200 × 600 px |
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| File format | OpenEXR|
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| HDR normalization | Radiance at reference shutter speed (1/500 – 1/10 s, per scene) |
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## Equipment
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- **Main camera**: Sony α7 series — RAW
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- **360° camera**: Insta360 Pro2 — 6-lens equidistant fisheye, stitched to equirectangular
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## Dataset Variants
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Two resolution variants are provided under `ori/` and `small/`. Within each variant, every scene contains four folders:
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```
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{variant}/{scene}/
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├── photo/ # HDR photograph (unaligned)
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├── photo_aligned/ # HDR photograph (temporally aligned, recommended)
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├── envmap/ # HDR environment map (unaligned)
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└── envmap_aligned/ # HDR environment map (temporally aligned, recommended)
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```
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Both `photo_aligned/` and `envmap_aligned/` are temporally aligned to the first time step — each image is warped so that the static background is registered across time. Use these for relighting experiments.
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## File Naming
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```
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photo/ time{N}_hdr.exr (N = 0, 1, 2, …)
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envmap/ time{N}_envmap.exr
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```
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## Metadata
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Each scene contains a `meta.json` with:
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| Field | Description |
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|-------|-------------|
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| `scene` | Scene name |
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| `ref_shutter` | Reference shutter speed (seconds) used for HDR normalization |
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| `ref_shutter_fraction` | Human-readable, e.g. `"1/500"` |
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| `hdr_method` | HDR merge algorithm (`triangular_weight_debevec1997`) |
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| `alignment_method` | `life_optical_flow` or `cv2_sift_homography` |
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| `gps` | `{latitude, longitude, altitude_m}` — available for 10 scenes |
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| `photos[].shooting_time` | Capture timestamp from EXIF |
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| `photos[].iso` | ISO speed |
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| `photos[].shutter_speed` | Representative shutter speed (human-readable) |
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| `envmaps[].shooting_time` | Capture timestamp |
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| `envmaps[].shutter_speed` | Envmap shutter speed |
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## Scene List
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GPS coordinates are provided as additional information. Since collecting GPS coordinates was not part of the original plan, only 10 scenes contain GPS coordinates.
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| Scene | Time steps | Alignment | GPS |
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|-------|-----------|-----------|-----|
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| bench2 | 6 | LIFE flow | — |
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| bench3 | 6 | LIFE flow | — |
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| bike | 6 | LIFE flow | yes |
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| bikes2 | 6 | LIFE flow | — |
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| bridge | 6 | LIFE flow | — |
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| building304 | 6 | LIFE flow | — |
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| building324 | 7 | LIFE flow | — |
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| byway | 6 | LIFE flow | yes |
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| cabin | 6 | CV2/SIFT | — |
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| corridor | 6 | LIFE flow | yes |
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| foodtruck | 6 | LIFE flow | yes |
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| garden | 6 | LIFE flow | — |
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| grassland | 7 | LIFE flow | yes |
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| grassland2 | 6 | LIFE flow | yes |
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| indoor | 6 | CV2/SIFT | — |
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| lake | 6 | LIFE flow | — |
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| meadow | 6 | LIFE flow | — |
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| parking | 7 | LIFE flow | — |
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| pillar | 6 | LIFE flow | — |
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| playground | 7 | LIFE flow | — |
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| rail | 7 | LIFE flow | — |
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| road | 7 | LIFE flow | — |
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| sand | 6 | CV2/SIFT | — |
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| seaside | 8 | CV2/SIFT | — |
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| stairs | 6 | LIFE flow | yes |
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| statue | 6 | LIFE flow | yes |
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| statue2 | 6 | LIFE flow | yes |
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| table_tennis | 6 | LIFE flow | yes |
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| wall | 6 | LIFE flow | — |
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| yatai | 7 | LIFE flow | — |
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## Temporal Alignment
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Photos are aligned to `time0` of each scene. Two methods are used depending on scene content:
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- **LIFE optical flow** (26 scenes): dense flow-based warping, handles gradual lighting changes and small camera motion
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- **CV2/SIFT homography** (4 scenes: `cabin`, `indoor`, `sand`, `seaside`): feature-based homography, used when flow alignment fails due to detailed textures.
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Alignment is computed independently for `ori` and `small` resolutions. Environment maps are aligned using phase-correlation-based rotation estimation.
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## License
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This dataset is released under the [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/) license.
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<!-- ## Citation
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If you use this dataset, please cite:
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```bibtex
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@inproceedings{wildrelight2026,
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title = {TODO},
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author = {TODO},
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booktitle = {TODO},
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year = {2026},
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}
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``` -->
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