File size: 7,202 Bytes
ba4507b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
125a296
ba4507b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
125a296
ba4507b
 
 
125a296
 
efed352
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
4461d3c
efed352
ba4507b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
7c99aea
ba4507b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
125a296
ba4507b
 
 
 
 
 
 
125a296
 
ba4507b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
---
license: apache-2.0
task_categories:
  - image-segmentation
  - object-detection
  - robotics
language:
  - en
tags:
  - robotics
  - navigation
  - frontiers
  - autonomous-systems
  - field-robotics
  - vision-foundation-models
  - outdoor-navigation
  - traversability
  - exploration
pretty_name: WildOS Frontiers Dataset
size_categories:
  - n<1K
configs:
  - config_name: default
    data_files:
      - split: train
        path: "**"
---

# WildOS Frontiers Dataset

<div align="center">
  <img src="https://leggedrobotics.github.io/wildos/static/images/Teaser-V.svg" alt="WildOS Teaser" width="800"/>
</div>

## Dataset Description

This dataset provides **visual frontier annotations** for outdoor long-range navigation, created for [WildOS: Open-Vocabulary Object Search in the Wild](https://leggedrobotics.github.io/wildos/). The annotations are built on top of images from the [GrandTour Dataset](https://huggingface.co/datasets/leggedrobotics/grand_tour_dataset).

**Visual Frontiers** denote regions in the image that correspond to candidate locations for further exploration — such as the end of a trail, an opening
between trees, or a road turning at a curve. This dataset enables training of models to predict visual frontiers from RGB images, extending navigation reasoning beyond the geometric depth horizon.

## Dataset Structure

```
wildos/
├── annotations/           # Frontier annotations (362 JSON files)
│   └── annotation_00000.json ... annotation_00389.json
├── RGB_frames/            # Raw RGB frames (390 images + metadata)
│   ├── metadata.json      # Maps to original GrandTour images
│   └── rgb_00000.png ... rgb_00389.png
├── RGB_rectified/         # Rectified RGB images (390 images)
│   └── rect_00000.png ... rect_00389.png
└── SAM_boundaries/        # SAM-2 boundary masks (390 images)
    └── bound_00000.png ... bound_00389.png
```

### File Descriptions

| Folder | Description | Count |
|--------|-------------|-------|
| `annotations/` | JSON files containing frontier bounding box annotations | 362 |
| `RGB_frames/` | Original RGB frames from GrandTour dataset | 390 + 1 metadata |
| `RGB_rectified/` | Rectified (undistorted) RGB images | 390 |
| `SAM_boundaries/` | Binary masks from SAM-2 boundary detection | 390 |

> **Note:** Some images do not have corresponding annotations (362 out of 390 images are annotated). Images without annotations were excluded during quality control. The `SAM_boundaries/` folder contains SAM-2 boundary masks used in an ablation study, where frontiers were defined as the SAM boundary segments within human-annotated bounding boxes.

## Annotation Format

Each annotation file contains a list of frontier detections with the following structure:

```json
[
  {
    "label": "frontier",
    "start": [1326.0, 618.0],
    "end": [1352.0, 636.0]
  }
]
```

| Field | Description |
|-------|-------------|
| `label` | Frontier label (currently `"frontier"` for all annotations) |
| `start` | Top-left corner `[x, y]` of the bounding box |
| `end` | Bottom-right corner `[x, y]` of the bounding box |

> **Note:** The `label` field exists because we initially experimented with labeling frontiers of varying strengths. In the final dataset, all annotations use the single label `"frontier"`.

## Example Annotations

<div align="center">
<table>
<tr>
<td><img src="https://leggedrobotics.github.io/wildos/static/images/label_examples/rect_00001.png" width="400"/></td>
<td><img src="https://leggedrobotics.github.io/wildos/static/images/label_examples/rect_00024.png" width="400"/></td>
</tr>
<tr>
<td><img src="https://leggedrobotics.github.io/wildos/static/images/label_examples/rect_00037.png" width="400"/></td>
<td><img src="https://leggedrobotics.github.io/wildos/static/images/label_examples/rect_00086.png" width="400"/></td>
</tr>
<tr>
<td><img src="https://leggedrobotics.github.io/wildos/static/images/label_examples/rect_00191.png" width="400"/></td>
<td><img src="https://leggedrobotics.github.io/wildos/static/images/label_examples/rect_00264.png" width="400"/></td>
</tr>
</table>
</div>

*Red regions indicate visual frontiers — candidate locations for further exploration.* More examples can be viewed [here](https://leggedrobotics.github.io/wildos/#frontier-annotations).

## Usage

### Loading Individual Files

```python
import json
from PIL import Image

# Load an annotation
with open("wildos/annotations/annotation_00000.json", "r") as f:
    annotations = json.load(f)

# Load corresponding image
image = Image.open("wildos/RGB_rectified/rect_00000.png")

print(f"Image size: {image.size}")
print(f"Number of frontiers: {len(annotations)}")
```

### Visualizing Annotations

Visualize frontier annotations on images:

```python
import os
import json
import cv2
import numpy as np

def visualize_frontiers(image_path, annotation_path, output_path=None):
    """Draw frontier annotations on an image."""
    # Load image
    img = cv2.imread(image_path)
    
    # Load annotations
    with open(annotation_path, "r") as f:
        annotations = json.load(f)
    
    # Draw each frontier
    for ann in annotations:
        x1, y1 = int(ann["start"][0]), int(ann["start"][1])
        x2, y2 = int(ann["end"][0]), int(ann["end"][1])
        color = (0, 0, 255)  # Red in BGR
        
        # Draw semi-transparent rectangle
        overlay = img.copy()
        cv2.rectangle(overlay, (x1, y1), (x2, y2), color, -1)
        cv2.addWeighted(overlay, 0.35, img, 0.65, 0, img)
        cv2.rectangle(img, (x1, y1), (x2, y2), color, 2)
    
    if output_path:
        cv2.imwrite(output_path, img)
    
    return img

# Example usage
visualize_frontiers(
    "wildos/RGB_rectified/rect_00000.png",
    "wildos/annotations/annotation_00000.json",
    "output_visualization.png"
)
```

### Metadata Mapping

The `metadata.json` file in `RGB_frames/` maps each image index to its source path in the GrandTour dataset:

```python
import json

with open("wildos/RGB_frames/metadata.json", "r") as f:
    metadata = json.load(f)

# Find original GrandTour image for a specific frame index
original_path = metadata["0"]  # e.g., "release_2024-11-03-07-57-34/hdr_front/hdr_front_01342.png"
print(f"Original GrandTour path: {original_path}")
```

## Related Resources

- **Project Page**: [WildOS: Open-Vocabulary Object Search in the Wild](https://leggedrobotics.github.io/wildos/)
- **Source Dataset**: [GrandTour Dataset](https://huggingface.co/datasets/leggedrobotics/grand_tour_dataset)

## Citation

If you use this dataset in your research, please cite:

```bibtex
@misc{shah2026wildosopenvocabularyobjectsearch,
        title={WildOS: Open-Vocabulary Object Search in the Wild}, 
        author={Hardik Shah and Erica Tevere and Deegan Atha and Marcel Kaufmann and Shehryar Khattak and Manthan Patel and Marco Hutter and Jonas Frey and Patrick Spieler},
        year={2026},
        eprint={2602.19308},
        archivePrefix={arXiv},
        primaryClass={cs.RO},
        url={https://arxiv.org/abs/2602.19308}, 
}
```

## License

This dataset is released under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0).