README.md

---
license: cc-by-4.0
dataset_info:
- config_name: default
  features:
  - name: frame_id
    dtype: int32
  - name: orig_frame
    dtype: string
  - name: image
    dtype: image
  - name: image_raw
    dtype: binary
  - name: albedo
    dtype: image
  - name: albedo_raw
    dtype: binary
  - name: depth
    dtype: image
  - name: depth_raw
    dtype: binary
  - name: normal
    dtype: image
  - name: normal_raw
    dtype: binary
  - name: mask
    dtype: image
  - name: camera
    dtype: string
  - name: sky_raw
    dtype: binary
  - name: model
    dtype: string
  - name: scene
    dtype: string
  - name: date
    dtype: string
  - name: lighting
    dtype: string
  splits:
  - name: train
    num_bytes: 48468861351
    num_examples: 5664
  - name: test
    num_bytes: 9440057784
    num_examples: 520
  - name: train_selected
    num_bytes: 21972048865
    num_examples: 2487
  download_size: 79310118605
  dataset_size: 79880968000
- config_name: models
  features:
  - name: model_name
    dtype: string
  - name: scene
    dtype: string
  - name: date
    dtype: string
  - name: lighting
    dtype: string
  - name: model_raw
    dtype: binary
  splits:
  - name: train
    num_bytes: 7294616740
    num_examples: 27
  download_size: 3751431547
  dataset_size: 7294616740
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
  - split: train_selected
    path: data/train_selected-*
- config_name: models
  data_files:
  - split: train
    path: models/train-*
---

# Olbedo: An Albedo and Shading Aerial Dataset for Large-Scale Outdoor Environments

[Shuang Song](https://openreview.net/profile?id=~Shuang_Song10)¹‡, 
[Debao Huang](https://debaohuang.github.io/)¹‡, 
[Deyan Deng](https://openreview.net/profile?id=~Deyan_Deng1)¹, 
[Haolin Xiong](https://openreview.net/profile?id=~Haolin_Xiong1)², 
[Yang Tang](https://openreview.net/profile?id=~Yang_Tang4)¹, 
[Yajie Zhao](https://openreview.net/profile?id=~Yajie_Zhao1)², 
[Rongjun Qin](https://openreview.net/profile?id=~Rongjun_Qin1)¹*

¹ The Ohio State University · ² University of Southern California  
‡ Equal contribution · * Corresponding author

This repository contains the Olbedo dataset, containing RGB images, albedo, depth, surface normals, camera parameters, sky HDR maps, and 3D scene models.

**HuggingFace:** [GDAOSU/Olbedo](https://huggingface.co/datasets/GDAOSU/Olbedo)

## Dataset Structure

### Main config (default)

Three splits: `train` (5,664 samples), `train_selected` (2,487 samples), `test` (520 samples).

| Column | Type | Description |
|--------|------|-------------|
| `frame_id` | int | Unique frame identifier |
| `orig_frame` | string | Original frame number |
| `image` | Image | sRGB preview (PNG, for visualization) |
| `image_raw` | binary | Original EXR file (Linear RGB, for training) |
| `albedo` | Image | sRGB preview (PNG, for visualization) |
| `albedo_raw` | binary | Original EXR file (Linear RGB, for training) |
| `depth` | Image | Colormap preview (PNG, INFERNO colormap) |
| `depth_raw` | binary | Original EXR file (float, for training) |
| `normal` | Image | RGB preview (PNG, remapped from [-1,1] to [0,1]) |
| `normal_raw` | binary | Original EXR file (float RGB, for training) |
| `mask` | Image | Segmentation mask (PNG) |
| `camera` | string | Camera parameters (JSON string) |
| `sky_raw` | binary | Sky HDR map (.hdr), `None` if not available |
| `model` | string | GLB model filename (e.g. `scene_date_lighting.glb`) |
| `scene` | string | Scene name |
| `date` | string | Capture date (YYYYMMDD) |
| `lighting` | string | Lighting condition (sunrise/sunset/overcast) |

The `test` split has `None` for depth, normal, camera, sky, and model columns.

### Models config

A separate config containing 27 3D scene models in GLB format.

| Column | Type | Description |
|--------|------|-------------|
| `model_name` | string | Model identifier (e.g. `osu_coe_corridors_20220720_sunrise`) |
| `scene` | string | Scene name |
| `date` | string | Capture date |
| `lighting` | string | Lighting condition |
| `model_raw` | binary | GLB file (binary) |

## Usage

### Load the dataset

```python
from datasets import load_dataset

# Load a split (downloads data)
ds = load_dataset("GDAOSU/Olbedo", split="train")

# Or use streaming to avoid downloading everything
ds = load_dataset("GDAOSU/Olbedo", split="train", streaming=True)
row = next(iter(ds))
```

### Recover raw EXR files (image, albedo, depth, normal)

```python
ds = load_dataset("GDAOSU/Olbedo", split="train", streaming=True)

for row in ds:
    fid = row['frame_id']

    # Save image EXR
    with open(f"{fid:04d}_im.exr", "wb") as f:
        f.write(row['image_raw'])

    # Save albedo EXR
    with open(f"{fid:04d}_albedo.exr", "wb") as f:
        f.write(row['albedo_raw'])

    # Save depth EXR
    if row['depth_raw'] is not None:
        with open(f"{fid:04d}_depth.exr", "wb") as f:
            f.write(row['depth_raw'])

    # Save normal EXR
    if row['normal_raw'] is not None:
        with open(f"{fid:04d}_normal.exr", "wb") as f:
            f.write(row['normal_raw'])
```

### Recover camera JSON

```python
import json

row = next(iter(ds))
if row['camera'] is not None:
    camera = json.loads(row['camera'])
    print(camera['focal'], camera['cx'], camera['cy'])  # intrinsics
    print(camera['X'], camera['Y'], camera['Z'])        # translation

    # Save to file
    fid = row['frame_id']
    with open(f"{fid:04d}_camera.json", "w") as f:
        f.write(row['camera'])
```

### Recover sky HDR maps

Only some frames have sky HDR maps (frames 3801+).

```python
for row in ds:
    if row['sky_raw'] is not None:
        fid = row['frame_id']
        with open(f"{fid:04d}_sky.hdr", "wb") as f:
            f.write(row['sky_raw'])
```

### Recover 3D models (GLB)

Models are stored in a separate config. Each model corresponds to a unique scene/date/lighting combination.

```python
models = load_dataset("GDAOSU/Olbedo", "models", split="train", streaming=True)

for row in models:
    name = row['model_name']
    with open(f"{name}.glb", "wb") as f:
        f.write(row['model_raw'])
    print(f"Saved {name}.glb ({len(row['model_raw']) / 1e6:.0f} MB)")
```

### Link frames to their 3D model

Each frame's `model` field contains the GLB filename. To find which model a frame belongs to:

```python
row = next(iter(ds))
print(row['model'])  # e.g. "osu_coe_corridors_20220720_sunrise.glb"
```

### Recover all data for a single frame

```python
import json

ds = load_dataset("GDAOSU/Olbedo", split="train", streaming=True)
row = next(iter(ds))
fid = row['frame_id']
prefix = f"{fid:04d}"

# Save all files
with open(f"{prefix}_im.exr", "wb") as f:
    f.write(row['image_raw'])
with open(f"{prefix}_albedo.exr", "wb") as f:
    f.write(row['albedo_raw'])
if row['depth_raw']:
    with open(f"{prefix}_depth.exr", "wb") as f:
        f.write(row['depth_raw'])
if row['normal_raw']:
    with open(f"{prefix}_normal.exr", "wb") as f:
        f.write(row['normal_raw'])
if row['camera']:
    with open(f"{prefix}_camera.json", "w") as f:
        f.write(row['camera'])
if row['sky_raw']:
    with open(f"{prefix}_sky.hdr", "wb") as f:
        f.write(row['sky_raw'])

# Save mask from preview Image
if row['mask'] is not None:
    row['mask'].save(f"{prefix}_mask.png")

print(f"Frame {fid}: scene={row['scene']}, date={row['date']}, lighting={row['lighting']}, model={row['model']}")
```

## Scenes

The dataset covers 4 locations with multiple captures under different dates and lighting conditions:

- `goodale_park`
- `osu_coe_corridors`
- `osu_residential_area`
- `schottenstein_center`

## File Formats

- **Image/Albedo EXR**: Linear RGB, float16/float32. Apply sRGB transfer function for display.
- **Depth EXR**: Single-channel float (channel name `I`). Use OpenEXR library to read.
- **Normal EXR**: RGB float in [-1, 1] range. Remap to [0, 1] for visualization.
- **Sky HDR**: Radiance HDR format (.hdr).
- **3D Models**: glTF Binary (.glb).
- **Camera JSON**: Contains intrinsics (focal, cx, cy, distortion), extrinsics (rotation matrix, translation), and metadata (GPS, sun position).