--- 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
WildOS Teaser
## 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
*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).