Update README.md

README.md

@@ -57,7 +57,7 @@ dataset_summary: '
 
   # Note: other available arguments include ''max_samples'', etc
 
-  dataset = load_from_hub("harpreetsahota/kitscenes-multimodal")
+  dataset = load_from_hub("Voxel51/kitscenes-multimodal")
 
 
   # Launch the App
@@ -71,10 +71,7 @@ dataset_summary: '
 
 # Dataset Card for kitscenes-multimodal
 
-<!-- Provide a quick summary of the dataset. -->
-
-
-
+![image/png](kitscenes.gif)
 
 
 This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 680 samples.
@@ -95,141 +92,223 @@ from fiftyone.utils.huggingface import load_from_hub
 
 # Load the dataset
 # Note: other available arguments include 'max_samples', etc
-dataset = load_from_hub("harpreetsahota/kitscenes-multimodal")
+dataset = load_from_hub("Voxel51/kitscenes-multimodal")
 
 # 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:** cc-by-nc-4.0
-
-### Dataset Sources [optional]
-
-<!-- Provide the basic links for the dataset. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+# KITScenes Multimodal — FiftyOne Dataset
+
+A FiftyOne build of **KITScenes Multimodal** (KIT-MRT), a high-fidelity European
+urban autonomous-driving dataset. Each frame is a synchronized capture from a
+full robotaxi sensor suite — nine global-shutter cameras giving 360° coverage,
+seven long-range lidars, and three 4D imaging radars — paired with production-grade
+Lanelet2 HD-map labels, projected lidar depth, the future ego path, and image
+instance predictions.
+
+This build packages those captures as a **grouped FiftyOne dataset** so every
+sensor for a given moment lives in one group, and the 3D lidar/radar point cloud
+sits alongside the camera images. The card below describes exactly what is in the
+dataset and how it is organized.
+
+## At a glance
+
+| | |
+|---|---|
+| **Dataset name** | `kitscenes-multimodal` |
+| **Media type** | `group` (grouped dataset) |
+| **Samples** | 6,800 |
+| **Frames (groups)** | 680 |
+| **Scenes** | 4 (validation split) |
+| **Frames per scene** | 100 / 100 / 200 / 280 |
+| **Group slices** | 9 cameras + 1 fused 3D lidar slice |
+| **Capture rate** | 10 Hz |
+| **Region** | Frankfurt, Germany (European urban) |
+| **License** | CC-BY-NC-4.0 |
+
+A *group* corresponds to one timestamped frame and holds 10 samples: the 9 camera
+images plus the fused 3D point cloud. With 680 groups that gives 6,120 image
+samples + 680 3D samples = 6,800 total.
+
+## Dataset sources
+
+- **Curated by:** the KITScenes team at the Institute of Measurement and Control
+  Systems (MRT), Karlsruhe Institute of Technology (KIT), and the FZI Research
+  Center for Information Technology — Richard Schwarzkopf and Fabian Immel (joint
+  first authors), Jan-Hendrik Pauls (project lead), Christoph Stiller, and
+  collaborators. This FiftyOne build was prepared by Harpreet Sahota (Voxel51).
+- **Language:** English
+- **License:** CC-BY-NC-4.0
+
+| Resource | Link |
+|---|---|
+| Original dataset (Hugging Face) | [KIT-MRT/KITScenes-Multimodal](https://huggingface.co/datasets/KIT-MRT/KITScenes-Multimodal) |
+| Single-scene preview (Hugging Face) | [KIT-MRT/KITScenes-Multimodal-Sample](https://huggingface.co/datasets/KIT-MRT/KITScenes-Multimodal-Sample) |
+| Python API / devkit (GitHub) | [KIT-MRT/kitscenes](https://github.com/KIT-MRT/kitscenes) |
+| Paper | *The Road Ahead in Autonomous Driving: The KITScenes Multimodal Dataset* — [arXiv:2606.02956](https://arxiv.org/abs/2606.02956) |
+| Project page | [kitscenes.com/multimodal](https://kitscenes.com/multimodal/) |
+| This FiftyOne build | `harpreetsahota/kitscenes-multimodal` (Hugging Face) |
+
+The `kitscenes` Python package on GitHub (the devkit) is the official loader for the
+sensor, calibration, and map data; this FiftyOne build uses it to decode and project
+the geometry and labels.
+
+## Dataset structure
+
+### Group slices
+
+The dataset is grouped on the `group` field. Each frame contains the following
+slices (the slice name doubles as the sensor name in the `sensor` field). The
+default slice shown in the App is `camera_ring_front`.
+
+| Slice | Media | Role |
+|---|---|---|
+| `camera_ring_front` | image | Forward ring camera (default view) |
+| `camera_ring_front_left` | image | Ring camera, front-left |
+| `camera_ring_front_right` | image | Ring camera, front-right |
+| `camera_ring_rear` | image | Rear ring camera |
+| `camera_ring_rear_left` | image | Ring camera, rear-left |
+| `camera_ring_rear_right` | image | Ring camera, rear-right |
+| `camera_base_front_center` | image | High-resolution long-range front camera |
+| `camera_base_front_left_rect` | image | Rectified front stereo, left |
+| `camera_base_front_right_rect` | image | Rectified front stereo, right |
+| `lidar` | 3d | Fused point cloud: 7 lidars + 3 radars, in the ego frame |
+
+The six `camera_ring_*` slices form the 360° surround view; the three
+`camera_base_*` slices are the long-range and stereo cameras.
+
+### Sample-level fields
+
+These fields are present on every sample (cameras and the 3D slice), giving each
+sample its scene context, timing, and ego pose.
+
+| Field | Type | Description |
+|---|---|---|
+| `scene_id` | string | UUID of the source scene |
+| `frame` | int | Frame index within the scene (0-based) |
+| `timestamp` | float | Reference timestamp (seconds) |
+| `sensor` | string | Sensor / slice name |
+| `ego_translation` | list[float] | Ego position `[x, y, z]` in the world frame |
+| `ego_quaternion` | list[float] | Ego orientation `[qx, qy, qz, qw]` |
+| `ego_yaw_deg` | float | Ego heading (degrees) |
+| `location` | `GeoLocation` | GNSS longitude/latitude |
+| `altitude` | float | GNSS altitude (meters) |
+| `gnss_fix_status` | int | GNSS fix-status code |
+| `ego_speed` | float | Ego speed from GNSS twist (m/s) |
+
+The per-frame ego pose plus GNSS together give the full **car trajectory** — the
+sequence of ego positions and headings over each scene.
+
+Camera slices additionally carry:
+
+| Field | Type | Description |
+|---|---|---|
+| `intrinsics` | dict | Pinhole intrinsics (focal length, principal point) |
+| `resolution` | dict | Image `width` / `height` |
+
+### Label fields
+
+Labels are attached per camera slice; not every label exists on every camera. The
+table shows where each one is populated.
+
+| Field | FiftyOne type | Where | What it is |
+|---|---|---|---|
+| `lidar_depth` | `Heatmap` | all 9 cameras | Fused lidar depth projected into the image, encoded as an 8-bit depth heatmap (near→far) |
+| `hd_map` | `Polylines` | 6 ring cameras | Lanelet2 HD-map elements reprojected into the image (lane markings, borders, road markings, poles, traffic signs, traffic lights) |
+| `ego_trajectory` | `Keypoints` | `camera_ring_front` | The vehicle's future path (ego waypoints) projected onto the road ahead, label `ego_path` |
+| `seamseg` | `Detections` | `camera_ring_front`, `camera_ring_rear` | Instance predictions (boxes + masks) in the Mapillary-Vistas taxonomy |
+
+`hd_map` polylines carry a top-level `label` (the coarse category) and a `subtype`
+attribute holding the fine-grained Lanelet2 class (e.g. lane-marking style, or the
+specific German traffic-sign code such as `de206`).
+
+### The 3D lidar slice
+
+The `lidar` slice is a single `.fo3d` scene per frame that fuses **seven lidars and
+three radars** into one ego-frame point cloud (lidar sweeps are motion-deskewed;
+radar detections are ego-motion compensated). Points are shaded by intensity in the
+App. The point clouds carry these per-point scalar fields:
+
+- **Lidar points:** `intensity` (reflectivity) and `isground` (per-point ground
+  flag from ground segmentation).
+- **Radar points:** `intensity` (RCS) and `range_rate` (Doppler velocity).
+
+### Saved views
+
+Three dynamic grouped views ship with the dataset for browsing:
+
+| View | What it shows |
+|---|---|
+| `ring_front_by_scene_frame` | The forward ring camera, grouped by `(scene_id, frame)` — 680 groups |
+| `ring_rear_by_scene_frame` | The rear ring camera, grouped by `(scene_id, frame)` — 680 groups |
+| `lidar_by_scene` | The fused lidar slice grouped by `scene_id` — 4 groups, one per scene |
+
+## Label taxonomies
+
+**HD map (`hd_map`) categories:** `lane_marking`, `road_marking`, `road_border`,
+`pole`, `traffic_sign`, `traffic_light`. Each polyline's `subtype` holds the
+detailed Lanelet2 class — lane-marking styles (e.g. `dashed`, `solid`,
+`dashed_solid`) and the fine-grained German traffic-sign codes (`de…`).
+
+**Instance predictions (`seamseg`) classes:** Mapillary-Vistas "thing" classes,
+including `Car`, `Truck`, `Bus`, `Bicycle`, `Motorcycle`, `Trailer`,
+`Other Vehicle`, `Person`, `Bicyclist`, `Motorcyclist`, `Other Rider`,
+`Traffic Light`, `Traffic Sign (Front)`, `Traffic Sign (Back)`,
+`Traffic Sign Frame`, `Pole`, `Utility Pole`, `Street Light`, `Bench`,
+`Billboard`, `Banner`, `Bike Rack`, `Trash Can`, `Mailbox`, `Fire Hydrant`,
+`Junction Box`, `Catch Basin`, `Manhole`, `Phone Booth`, `CCTV Camera`, `Bird`,
+`Wheeled Slow`, `Crosswalk - Plain`, `Lane Marking - Crosswalk`.
 
 ## 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. -->
-
-[More Information Needed]
-
-## Dataset Creation
-
-### Curation Rationale
-
-<!-- Motivation for the creation of this dataset. -->
-
-[More Information Needed]
-
-### Source Data
-
-<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
-
-#### Data Collection and Processing
-
-<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
-
-[More Information Needed]
-
-#### Who are the source data producers?
-
-<!-- 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. -->
-
-[More Information Needed]
-
-### Annotations [optional]
-
-<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
-
-#### Annotation process
-
-<!-- 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. -->
-
-[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]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Dataset Card Authors [optional]
-
-[More Information Needed]
+This FiftyOne build is suited to:
+
+- **Multimodal browsing and curation** — inspect all 9 cameras and the fused
+  point cloud for any frame, side by side.
+- **HD-map perception** — the `hd_map` polylines provide reprojection-accurate
+  Lanelet2 map labels aligned to image pixels.
+- **Long-range depth** — `lidar_depth` heatmaps provide dense, long-range depth
+  ground truth (the source lidar reaches beyond 400 m).
+- **Trajectory / motion work** — per-frame ego pose plus the projected
+  `ego_trajectory` future path.
+- **2D object analysis** — the `seamseg` instance detections on the front and rear
+  ring cameras.
+
+### Out-of-scope
+
+This is an early-release **preview** subset (4 validation scenes). It is meant for
+exploration and pipeline development, not final benchmark reporting. The build also
+does **not** include 3D bounding boxes, tracks, or instance segmentation for
+dynamic agents (the source dataset omits these in the current release). The
+`seamseg` detections are model predictions, not human annotations.
+
+## Source data
+
+KITScenes Multimodal was recorded across Karlsruhe, Frankfurt, and Sindelfingen by
+the Institute of Measurement and Control Systems (MRT) at the Karlsruhe Institute of
+Technology (KIT). The scenes here are from the validation split (Frankfurt). Camera
+imagery is anonymized (faces and license plates). Geometry and label projections in
+this build are produced with the official `kitscenes` Python API. See
+[Dataset sources](#dataset-sources) above for the original dataset, devkit, paper,
+and project-page links.
+
+## Citation
+
+```bibtex
+@misc{schwarzkopf2026kitscenes,
+      title={The Road Ahead in Autonomous Driving: The KITScenes Multimodal Dataset},
+      author={Richard Schwarzkopf and Fabian Immel and Alexander Blumberg and Jonas Merkert and Nils Rack and Kaiwen Wang and Fabian Konstantinidis and Julian Truetsch and Carlos Fernandez and Annika Bätz and Kevin Rösch and Marlon Steiner and Willi Poh and Yinzhe Shen and Royden Wagner and Felix Hauser and Dominik Strutz and Jaime Villa and Gleb Stepanov and Holger Caesar and Ömer Şahin Taş and Frank Bieder and Jan-Hendrik Pauls and Christoph Stiller},
+      year={2026},
+      eprint={2606.02956},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2606.02956},
+}
+```
 
-## Dataset Card Contact
+## License
 
-[More Information Needed]
+Released under [CC-BY-NC-4.0](https://creativecommons.org/licenses/by-nc/4.0/),
+matching the source dataset's terms. Non-commercial use only; attribution required.