README.md

---
annotations_creators: []
language: en
size_categories:
- 10K<n<100K
task_categories:
- image-classification
- object-detection
task_ids: []
pretty_name: stone_35
tags:
- fiftyone
- group
- image-classification
- object-detection
dataset_summary: '




  This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 7000 samples.


  ## Installation


  If you haven''t already, install FiftyOne:


  ```bash

  pip install -U fiftyone

  ```


  ## Usage


  ```python

  import fiftyone as fo

  from fiftyone.utils.huggingface import load_from_hub


  # Load the dataset

  # Note: other available arguments include ''max_samples'', etc

  dataset = load_from_hub("Voxel51/STONE")


  # Launch the App

  session = fo.launch_app(dataset)

  ```

  '
---

# Dataset Card for STONE

![image/png](stone.gif)


STONE is a large-scale multi-modal dataset for off-road 3D traversability prediction, collected by autonomous ground vehicles across four outdoor environments in South Korea. It provides 7,000 keyframes with surround-view imagery from 6 cameras (1904×1200), 128-channel LiDAR scans (230K points), and voxel-level traversability annotations classifying terrain into free, traversable, potentially traversable, and non-traversable regions. Following the nuScenes format, the dataset includes 3D obstacle bounding boxes, ego-pose trajectories, and synchronized multi-sensor data at ~10 Hz. This FiftyOne version contains a stratified sample of 35 scenes (200 frames each) from the full 279-scene collection, organized as grouped samples with 7 slices per keyframe (6 cameras + 1 LiDAR 3D scene).


This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 7000 samples.

## Installation

If you haven't already, install FiftyOne:

```bash
pip install -U fiftyone
```

## Usage

```python
import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub

# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = load_from_hub("Voxel51/STONE")

# Launch the App
session = fo.launch_app(dataset)
```


# STONE — FiftyOne Dataset Card

STONE is a large-scale multi-modal dataset for **off-road 3D traversability prediction**, collected by an autonomous ground vehicle (UGV) across four outdoor environments in South Korea. The dataset follows the nuScenes format and provides surround-view camera imagery, 128-channel LiDAR scans, and voxel-level traversability annotations.

- **Paper:** Park et al., *"STONE: A Scalable Multi-Modal Surround-View 3D Traversability Dataset for Off-Road Robot Navigation"*, ICRA 2026
- **arXiv:** https://arxiv.org/abs/2603.09175
- **License:** CC BY-NC-ND 4.0 (dataset) · Apache 2.0 (code)
- **Format:** nuScenes / Occ3D-nuScenes
- **Project Page: https://konyul.github.io/STONE-dataset/**


## FiftyOne Dataset Structure

The dataset is a **grouped dataset** — one group per keyframe, with seven slices:

| Slice | Media type | Content |
|---|---|---|
| `CAM_FRONT` | `image` | 1904 × 1200 JPEG, front-facing camera |
| `CAM_FRONT_LEFT` | `image` | 1904 × 1200 JPEG |
| `CAM_FRONT_RIGHT` | `image` | 1904 × 1200 JPEG |
| `CAM_BACK` | `image` | 1904 × 1200 JPEG |
| `CAM_BACK_LEFT` | `image` | 1904 × 1200 JPEG |
| `CAM_BACK_RIGHT` | `image` | 1904 × 1200 JPEG |
| `LIDAR_TOP` | `3d` | `.fo3d` scene (LiDAR + Traversability + Trajectory layers) |

## Sample Fields

These fields are present on **every sample** across all seven slices.

### Identity & Provenance

| Field | Type | Description |
|---|---|---|
| `channel` | `StringField` | Sensor name: `CAM_FRONT`, `CAM_BACK`, …, `LIDAR_TOP` |
| `sample_token` | `StringField` | nuScenes sample token (shared across all 7 slices in a group) |
| `scene_token` | `StringField` | nuScenes scene token |
| `scene_name` | `StringField` | Human-readable scene ID, e.g. `scene-0053` |
| `location` | `StringField` | Recording site: `siheung_lake`, `siheung_farmland`, `siheung_land`, `kwangmyeong_land` |
| `vehicle` | `StringField` | Vehicle ID: `n001` – `n004` |
| `timestamp` | `IntField` | Unix timestamp in microseconds |

### nuScenes Metadata (matching the official nuScenes guide)

| Field | Type | Description |
|---|---|---|
| `token` | `StringField` | `sample_data` token for this specific sensor record |
| `ego_pose_token` | `StringField` | Token into `ego_pose.json` — vehicle pose at this timestamp |
| `calibrated_sensor_token` | `StringField` | Token into `calibrated_sensor.json` — intrinsics & extrinsics |
| `is_key_frame` | `BooleanField` | Always `True` (STONE only contains keyframes) |
| `prev` | `StringField` | Previous `sample_data` token for this sensor (empty at scene start) |
| `next` | `StringField` | Next `sample_data` token for this sensor (empty at scene end) |
| `sample_prev` | `StringField` | Previous nuScenes sample token in the scene |
| `sample_next` | `StringField` | Next nuScenes sample token in the scene |

### Labels

| Field | Type | Slices | Description |
|---|---|---|---|
| `ground_truth` | `fo.Detections` | LIDAR_TOP | 3D obstacle annotations. Each `fo.Detection` carries `location=[x,y,z]`, `rotation=[roll,pitch,yaw]`, `dimensions=[l,w,h]` in the LiDAR sensor frame, plus `num_lidar_pts` and `instance_token` |
| `cuboids` | `fo.Polylines` | cameras | 3D bounding boxes projected onto each camera as wireframe outlines using `fo.Polyline.from_cuboid()`. Filtered to boxes with all corners in front of the camera |
| `ground_truth_2d` | `fo.Detections` | cameras | Flat 2D bounding boxes from the pre-computed `bbox_2d` field in `sample_annotation.json`. Normalised `[x, y, w, h]` in `[0, 1]` space |
| `terrain` | `fo.Classification` | all | Dominant traversability class in the frame's voxel grid. `label` ∈ `{free, traversable, potentially_traversable, non_traversable}`. `confidence` = fraction of labeled voxels in that class |
| `trajectory_2d` | `fo.Polylines` | cameras | Projected path of the next 30 ego-pose waypoints (~3 seconds ahead) into the camera image plane. Present on ~83% of frames (absent near scene end) |

### Traversability Fractions

These fields are on all slices, derived from `gts/<scene>/<token>/labels.npz`.

| Field | Type | Description |
|---|---|---|
| `pct_free` | `FloatField` | Fraction of labeled voxels classified as Free (class 0) |
| `pct_traversable` | `FloatField` | Fraction classified as Traversable (class 1) |
| `pct_potentially_traversable` | `FloatField` | Fraction classified as Potentially Traversable (class 2) |
| `pct_non_traversable` | `FloatField` | Fraction classified as Non-Traversable (class 3) |

---

## LIDAR_TOP `.fo3d` Scene

Each LIDAR_TOP sample points to a `.fo3d` scene file containing three stacked point cloud layers:

| Layer | Shading | Source | Description |
|---|---|---|---|
| `LiDAR` | `height` | `samples/LIDAR_TOP/*.pcd` | 230,400-point raw scan from Hesai OT128. Points coloured by Z elevation via the viridis colorscale |
| `Traversability` | `rgb` | `samples/VOXEL_OVERLAY/*_voxels.pcd` | ~140K points from the same scan, coloured by traversability class. Each point's class is looked up from the voxel grid after transforming from LiDAR sensor frame to ego frame |
| `Trajectory` | `rgb` | `samples/TRAJECTORY/*_traj.pcd` | All 200 ego-pose waypoints for the scene, transformed to the current frame's LiDAR sensor frame. Blue = past · White = current · Yellow = future |

Camera configuration: `defaultCameraPosition = {x: -15, y: 0, z: 10}` (15 m behind, 10 m above), `up = "Z"` (NuScenes Z-up convention), set via `dataset.app_config.plugins["3d"]`.

---

## Traversability Classes

| Class ID | Label | `terrain.label` value | Colour in viewer |
|---|---|---|---|
| 0 | Free | `free` | 🟢 green `rgb(50, 230, 50)` |
| 1 | Traversable | `traversable` | 🟡 yellow `rgb(230, 230, 50)` |
| 2 | Potentially Traversable | `potentially_traversable` | 🟠 orange `rgb(255, 153, 0)` |
| 3 | Non-Traversable | `non_traversable` | 🔴 red `rgb(230, 25, 25)` |

The voxel grid has shape `(200, 200, 16)` — a 40 m × 40 m × 3.2 m volume centred on the vehicle at 0.2 m resolution. Value `255` = unoccupied.

---


## Citation

```bibtex
@inproceedings{park2026stone,
  title={STONE: A Scalable Multi-Modal Surround-View 3D Traversability Dataset for Off-Road Robot Navigation},
  author={Park, Konyul and Kim, Daehun and Oh, Jiyong and Yu, Seunghoon and Park, Junseo
          and Park, Jaehyun and Shin, Hongjae and Cho, Hyungchan and Kim, Jungho and Choi, Jun Won},
  booktitle={Proceedings of the IEEE International Conference on Robotics and Automation (ICRA)},
  year={2026}
}
```