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---
license: cc-by-4.0
task_categories:
  - image-to-image
tags:
  - relighting
  - HDR
  - environment-map
  - outdoor
  - real-world
pretty_name: WildRelight
---

# WildRelight Dataset

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.

## Key Statistics

| Property | Value |
|----------|-------|
| Scenes | 30 |
| Time steps per scene | 6–8 (spaced ~1 hour apart) |
| Total HDR photo/envmap pairs | 196 |
| Photo resolution (full) | 6048 × 4024 px |
| Envmap resolution (full) | 7680 × 3840 px (equirectangular) |
| Photo resolution (small) | 1200 × ~798 px |
| Envmap resolution (small) | 1200 × 600 px |
| File format | OpenEXR|
| HDR normalization | Radiance at reference shutter speed (1/500 – 1/10 s, per scene) |

## Equipment

- **Main camera**: Sony α7 series — RAW
- **360° camera**: Insta360 Pro2 — 6-lens equidistant fisheye, stitched to equirectangular

## Dataset Variants

Two resolution variants are provided under `ori/` and `small/`. Within each variant, every scene contains four folders:

```
{variant}/{scene}/
├── photo/              # HDR photograph (unaligned)
├── photo_aligned/      # HDR photograph (temporally aligned, recommended)
├── envmap/             # HDR environment map (unaligned)
└── envmap_aligned/     # HDR environment map (temporally aligned, recommended)
```

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.

## File Naming

```
photo/          time{N}_hdr.exr          (N = 0, 1, 2, …)
envmap/         time{N}_envmap.exr
```

## Metadata

Each scene contains a `meta.json` with:

| Field | Description |
|-------|-------------|
| `scene` | Scene name |
| `ref_shutter` | Reference shutter speed (seconds) used for HDR normalization |
| `ref_shutter_fraction` | Human-readable, e.g. `"1/500"` |
| `hdr_method` | HDR merge algorithm (`triangular_weight_debevec1997`) |
| `alignment_method` | `life_optical_flow` or `cv2_sift_homography` |
| `gps` | `{latitude, longitude, altitude_m}` — available for 10 scenes |
| `photos[].shooting_time` | Capture timestamp from EXIF |
| `photos[].iso` | ISO speed |
| `photos[].shutter_speed` | Representative shutter speed (human-readable) |
| `envmaps[].shooting_time` | Capture timestamp |
| `envmaps[].shutter_speed` | Envmap shutter speed |

## Scene List

GPS coordinates are provided as additional information. Since collecting GPS coordinates was not part of the original plan, only 10 scenes contain GPS coordinates.


| Scene | Time steps | Alignment | GPS |
|-------|-----------|-----------|-----|
| bench2 | 6 | LIFE flow | — |
| bench3 | 6 | LIFE flow | — |
| bike | 6 | LIFE flow | yes |
| bikes2 | 6 | LIFE flow | — |
| bridge | 6 | LIFE flow | — |
| building304 | 6 | LIFE flow | — |
| building324 | 7 | LIFE flow | — |
| byway | 6 | LIFE flow | yes |
| cabin | 6 | CV2/SIFT | — |
| corridor | 6 | LIFE flow | yes |
| foodtruck | 6 | LIFE flow | yes |
| garden | 6 | LIFE flow | — |
| grassland | 7 | LIFE flow | yes |
| grassland2 | 6 | LIFE flow | yes |
| indoor | 6 | CV2/SIFT | — |
| lake | 6 | LIFE flow | — |
| meadow | 6 | LIFE flow | — |
| parking | 7 | LIFE flow | — |
| pillar | 6 | LIFE flow | — |
| playground | 7 | LIFE flow | — |
| rail | 7 | LIFE flow | — |
| road | 7 | LIFE flow | — |
| sand | 6 | CV2/SIFT | — |
| seaside | 8 | CV2/SIFT | — |
| stairs | 6 | LIFE flow | yes |
| statue | 6 | LIFE flow | yes |
| statue2 | 6 | LIFE flow | yes |
| table_tennis | 6 | LIFE flow | yes |
| wall | 6 | LIFE flow | — |
| yatai | 7 | LIFE flow | — |


## Temporal Alignment

Photos are aligned to `time0` of each scene. Two methods are used depending on scene content:

- **LIFE optical flow** (26 scenes): dense flow-based warping, handles gradual lighting changes and small camera motion
- **CV2/SIFT homography** (4 scenes: `cabin`, `indoor`, `sand`, `seaside`): feature-based homography, used when flow alignment fails due to detailed textures.

Alignment is computed independently for `ori` and `small` resolutions. Environment maps are aligned using phase-correlation-based rotation estimation.

## License

This dataset is released under the [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/) license.

<!-- ## Citation

If you use this dataset, please cite:

```bibtex
@inproceedings{wildrelight2026,
  title     = {TODO},
  author    = {TODO},
  booktitle = {TODO},
  year      = {2026},
}
``` -->