Update README.md

README.md

@@ -48,7 +48,7 @@ dataset_summary: '
 
   # Note: other available arguments include ''max_samples'', etc
 
-  dataset = load_from_hub("harpreetsahota/STONE")
+  dataset = load_from_hub("Voxel51/STONE")
 
 
   # Launch the App
@@ -60,12 +60,12 @@ dataset_summary: '
   '
 ---
 
-# Dataset Card for stone_35
-
-<!-- Provide a quick summary of the 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.
@@ -86,141 +86,126 @@ from fiftyone.utils.huggingface import load_from_hub
 
 # Load the dataset
 # Note: other available arguments include 'max_samples', etc
-dataset = load_from_hub("harpreetsahota/STONE")
+dataset = load_from_hub("Voxel51/STONE")
 
 # Launch the App
 session = fo.launch_app(dataset)
 ```
 
 
-## Dataset Details
-
-### Dataset Description
-
-<!-- Provide a longer summary of what this dataset is. -->
-
-
-
-- **Curated by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Language(s) (NLP):** en
-- **License:** [More Information Needed]
-
-### Dataset Sources [optional]
-
-<!-- Provide the basic links for the dataset. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the dataset is intended to be used. -->
-
-### Direct Use
-
-<!-- This section describes suitable use cases for the dataset. -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
-
-[More Information Needed]
-
-## Dataset Structure
-
-<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
+# STONE — FiftyOne Dataset Card
 
-[More Information Needed]
+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.
 
-## Dataset Creation
+- **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/**
 
-### Curation Rationale
 
-<!-- Motivation for the creation of this dataset. -->
+## FiftyOne Dataset Structure
 
-[More Information Needed]
+The dataset is a **grouped dataset** — one group per keyframe, with seven slices:
 
-### Source Data
+| 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) |
 
-<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
+## Sample Fields
 
-#### Data Collection and Processing
+These fields are present on **every sample** across all seven slices.
 
-<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
+### Identity & Provenance
 
-[More Information Needed]
+| 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 |
 
-#### Who are the source data producers?
+### nuScenes Metadata (matching the official nuScenes guide)
 
-<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
+| 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 |
 
-[More Information Needed]
+### Labels
 
-### Annotations [optional]
+| 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) |
 
-<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
+### Traversability Fractions
 
-#### Annotation process
+These fields are on all slices, derived from `gts/<scene>/<token>/labels.npz`.
 
-<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
+| 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) |
 
-[More Information Needed]
-
-#### Who are the annotators?
-
-<!-- This section describes the people or systems who created the annotations. -->
-
-[More Information Needed]
-
-#### Personal and Sensitive Information
-
-<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
+---
 
-[More Information Needed]
+## LIDAR_TOP `.fo3d` Scene
 
-**APA:**
+Each LIDAR_TOP sample points to a `.fo3d` scene file containing three stacked point cloud layers:
 
-[More Information Needed]
+| 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 |
 
-## Glossary [optional]
+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"]`.
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
+---
 
-[More Information Needed]
+## Traversability Classes
 
-## More Information [optional]
+| 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)` |
 
-[More Information Needed]
+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.
 
-## Dataset Card Authors [optional]
+---
 
-[More Information Needed]
 
-## Dataset Card Contact
+## Citation
 
-[More Information Needed]
+```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}
+}
+```