Datasets:

mohangrim
/

WayPOP

---
pretty_name: WayPOP
language:
  - en
license: other
license_name: waymo-and-source-asset-terms
license_link: https://waymo.com/open/terms/
size_categories:
  - 10K<n<100K
task_categories:
  - image-segmentation

task_ids:
  - panoptic-segmentation
  - semantic-segmentation
  - instance-segmentation

tags:
  - image
  - video
  - 3d
  - open-set-panoptic-segmentation
  - open-set-panoptic-tracking
  - panoptic-segmentation
  - panoptic-tracking
  - video-panoptic-segmentation
  - multi-object-tracking
  - tracking
  - open-set-recognition
  - out-of-distribution
  - multi-view
  - multi-camera
  - cross-camera-tracking
  - autonomous-driving
  - scene-understanding
  - lidar
  - point-cloud
  - rgb-lidar
  - benchmark
  - waymo-open-dataset
  - waymo-pvps
  - objaverse
  - synthetic-augmentation
 
---
# WayPOP: A Panoramic Open-Set Panoptic Tracking Benchmark

![Five camera views of a driving scene](assets/teaser.gif)

## Dataset Summary

WayPOP is a benchmark for panoramic open-set panoptic segmentation and tracking
in autonomous driving. It evaluates whether perception systems can segment
known classes and unknown objects while maintaining consistent instance
identities across time and camera views.

The dataset augments real driving sequences from the Waymo Panoramic Video
Panoptic Segmentation (PVPS) dataset with curated 3D objects from Objaverse 1.0
and Objaverse-XL. Inserted objects fall outside the benchmark’s known semantic
taxonomy and are labeled as **unknown**. They are placed at fixed locations in
the 3D scene and rendered consistently across synchronized camera views and
time, with corresponding updates to LiDAR measurements.

The release provides RGB images from five synchronized cameras, vehicle-frame
XYZ point clouds, camera calibration, timestamps, and poses. Validation images
include panoptic annotations with consistent instance identities across time
and views. Test annotations are withheld for evaluation through the benchmark
server.

See the accompanying [WayPOP paper](http://arxiv.org/abs/2611.05801) for dataset
construction, task definitions, and the evaluation protocol.

### Benchmark Evaluation

Evaluation will be hosted on Codabench through three task-specific competitions:

- **[Multi-View Open-Set Panoptic Tracking (MV-OSPT)](https://www.codabench.org/competitions/18460/):** Panoptic
  segmentation with consistent instance identities across time and all five
  camera views.
- **[Frontal-View Open-Set Panoptic Tracking (FV-OSPT)](https://www.codabench.org/competitions/18458/):** Open-set panoptic
  tracking in the front-facing camera, maintaining consistent instance
  identities over time.
- **[View-Independent Open-Set Panoptic Segmentation (VI-OSPS)](https://www.codabench.org/competitions/18459/):** Open-set
  panoptic segmentation of individual images without temporal or cross-camera
  association.

All three tasks evaluate both known classes and unknown objects.

## Access and Terms

To use the Waymo-derived data, register at [Waymo Open Dataset](https://waymo.com/open)
and accept the [Waymo Dataset License Agreement for Non-Commercial
Use](https://waymo.com/open/terms/). The inserted 3D assets retain their
individual source licenses. Read [TERMS_OF_USE](TERMS_OF_USE.md) before using
or sharing WayPOP; [LICENSE](LICENSE) provides attribution and upstream-license
details. WayPOP is not affiliated with Waymo LLC.

## Splits

The paper calls the two test splits WayPOP-v1 and WayPOP-XL. Their short IDs are
used in folder names, manifests, archives, and download commands.

| Public name | Split ID | Description |
| --- | --- | --- |
| Validation | `val` | Six Objaverse 1.0 sequence variants with public panoptic annotations. |
| WayPOP-v1 | `v1` | Test split with Objaverse 1.0 objects and private annotations. |
| WayPOP-XL | `xl` | Test split with Objaverse-XL objects and private annotations. |

## Dataset Structure

The release contains one metadata archive and one archive per sequence variant.
After extraction, files follow the layout below. Data directories use the split
IDs defined in [Splits](#splits).

```text
waypop/
├── README.md
├── FORMAT.md
├── LICENSE
├── TERMS_OF_USE.md
├── categories.json
├── release.json
├── sequences.jsonl
├── SHA256SUMS
├── assets/
│   └── teaser.gif
├── tools/
│   ├── read_sample.py
│   └── verify_download.py
├── val/
│   ├── images/
│   ├── pointcloud/
│   ├── panoptic/
│   ├── wstq_meta/
│   └── meta/
│       ├── samples.jsonl
│       └── sequences/
├── v1/
│   ├── images/
│   ├── pointcloud/
│   └── meta/
└── xl/
    ├── images/
    ├── pointcloud/
    └── meta/
```

### File Organization

RGB images (`images/`) and panoptic annotations (`panoptic/`) are organized by
sequence, camera, and frame. Point clouds (`pointcloud/`) are organized by
sequence and frame. All RGB images are stored as PNG files.

### Sample Metadata

Each data directory contains a `meta/samples.jsonl` file with one record per
camera-frame sample. Each record includes:

- **Identifiers:** Sample and sequence identifiers, source sequence, clip, and asset source.
- **Camera and frame information:** Camera information, frame index, timestamp, and image dimensions.
- **Data paths:** Paths to the corresponding RGB image and point cloud.
- **Poses:** Vehicle and image poses.

Validation records additionally include paths to panoptic annotations and
wSTQ metadata, together with the panoptic label divisor.

## Download

After access to [the WayPOP dataset repository](https://huggingface.co/datasets/mohangrim/WayPOP)
is granted, download and extract the complete release:

```bash
python -m pip install -U huggingface_hub
hf auth login
hf download mohangrim/WayPOP --repo-type dataset --local-dir waypop_download
python waypop_download/reconstruct_release.py waypop_download waypop
python waypop/tools/verify_download.py waypop
```

To download only validation data:

```python
from huggingface_hub import snapshot_download
snapshot_download(
    repo_id="mohangrim/WayPOP",
    repo_type="dataset",
    local_dir="waypop_download",
    allow_patterns=[
        "archive_index.json",
        "reconstruct_release.py",
        "README.md",
        "FORMAT.md",
        "TERMS_OF_USE.md",
        "waypop-2026-metadata.tar",
        "waypop-2026-val-*.tar",
    ],
)
```

```bash
python waypop_download/reconstruct_release.py waypop_download waypop --split val
python waypop/tools/verify_download.py waypop --split val
```

The extraction script checks archive hashes before writing files. Both tools
also accept `--sequence SEQUENCE_ID` for an individual variant.
Markdown documentation is copied from the download folder and can be updated
without changing the checksummed data archives.

## Reading the Data

See [FORMAT.md](FORMAT.md) for sample paths, sensor metadata, validation
annotations, and benchmark prediction and submission formats.

## Classes

[`categories.json`](categories.json) is the authoritative taxonomy and
benchmark category list. Predictions use these raw category IDs:

| Raw ID | Category | Type |
| ---: | --- | --- |
| 2 | car | thing |
| 3 | truck | thing |
| 4 | bus | thing |
| 5 | other_large_vehicle | thing |
| 6 | bicycle | stuff |
| 7 | motorcycle | stuff |
| 8 | trailer | thing |
| 9 | pedestrian | thing |
| 10 | cyclist | thing |
| 11 | motorcyclist | stuff |
| 14 | construction_cone_pole | stuff |
| 15 | pole | stuff |
| 16 | pedestrian_object | stuff |
| 17 | sign | stuff |
| 18 | traffic_light | stuff |
| 19 | building | stuff |
| 20 | road | stuff |
| 21 | lane_marker | stuff |
| 22 | road_marker | stuff |
| 23 | sidewalk | stuff |
| 24 | vegetation | stuff |
| 25 | sky | stuff |
| 29 | unknown | unknown/OOD |

## Citation

If you use WayPOP in your research, please cite our paper:

**[WayPOP: A Panoramic Open-Set Panoptic Tracking Benchmark](http://arxiv.org/abs/2611.05801)**
(NeurIPS, 2026)

```bibtex
@inproceedings{waypop2026,
  title={WayPOP: A Panoramic Open-Set Panoptic Tracking Benchmark},
  author={Mohan, Rohit and Luz, Maximilian and Chowdhury, Swarnava and Lu, Yao and Drews, Florian and N{\"u}rnberg, Thomas and Miron, Yakov and Tombari, Federico and Gasperini, Stefano and Valada, Abhinav},
  booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
  year={2026}
}
```

## Contact

For dataset questions, bug reports, benchmark submission issues, or
collaboration inquiries, contact Rohit Mohan at
[mohan@cs.uni-freiburg.de](mailto:mohan@cs.uni-freiburg.de).

	---
	pretty_name: WayPOP
	language:
	- en
	license: other
	license_name: waymo-and-source-asset-terms
	license_link: https://waymo.com/open/terms/
	size_categories:
	- 10K<n<100K
	task_categories:
	- image-segmentation

	task_ids:
	- panoptic-segmentation
	- semantic-segmentation
	- instance-segmentation

	tags:
	- image
	- video
	- 3d
	- open-set-panoptic-segmentation
	- open-set-panoptic-tracking
	- panoptic-segmentation
	- panoptic-tracking
	- video-panoptic-segmentation
	- multi-object-tracking
	- tracking
	- open-set-recognition
	- out-of-distribution
	- multi-view
	- multi-camera
	- cross-camera-tracking
	- autonomous-driving
	- scene-understanding
	- lidar
	- point-cloud
	- rgb-lidar
	- benchmark
	- waymo-open-dataset
	- waymo-pvps
	- objaverse
	- synthetic-augmentation

	---

	# WayPOP: A Panoramic Open-Set Panoptic Tracking Benchmark

	![Five camera views of a driving scene](assets/teaser.gif)

	## Dataset Summary

	WayPOP is a benchmark for panoramic open-set panoptic segmentation and tracking
	in autonomous driving. It evaluates whether perception systems can segment
	known classes and unknown objects while maintaining consistent instance
	identities across time and camera views.

	The dataset augments real driving sequences from the Waymo Panoramic Video
	Panoptic Segmentation (PVPS) dataset with curated 3D objects from Objaverse 1.0
	and Objaverse-XL. Inserted objects fall outside the benchmark’s known semantic
	taxonomy and are labeled as unknown. They are placed at fixed locations in
	the 3D scene and rendered consistently across synchronized camera views and
	time, with corresponding updates to LiDAR measurements.

	The release provides RGB images from five synchronized cameras, vehicle-frame
	XYZ point clouds, camera calibration, timestamps, and poses. Validation images
	include panoptic annotations with consistent instance identities across time
	and views. Test annotations are withheld for evaluation through the benchmark
	server.

	See the accompanying [WayPOP paper](http://arxiv.org/abs/2611.05801) for dataset
	construction, task definitions, and the evaluation protocol.

	### Benchmark Evaluation

	Evaluation will be hosted on Codabench through three task-specific competitions:

	- [Multi-View Open-Set Panoptic Tracking (MV-OSPT)](https://www.codabench.org/competitions/18460/): Panoptic
	segmentation with consistent instance identities across time and all five
	camera views.
	- [Frontal-View Open-Set Panoptic Tracking (FV-OSPT)](https://www.codabench.org/competitions/18458/): Open-set panoptic
	tracking in the front-facing camera, maintaining consistent instance
	identities over time.
	- [View-Independent Open-Set Panoptic Segmentation (VI-OSPS)](https://www.codabench.org/competitions/18459/): Open-set
	panoptic segmentation of individual images without temporal or cross-camera
	association.

	All three tasks evaluate both known classes and unknown objects.

	## Access and Terms

	To use the Waymo-derived data, register at [Waymo Open Dataset](https://waymo.com/open)
	and accept the [Waymo Dataset License Agreement for Non-Commercial
	Use](https://waymo.com/open/terms/). The inserted 3D assets retain their
	individual source licenses. Read [TERMS_OF_USE](TERMS_OF_USE.md) before using
	or sharing WayPOP; [LICENSE](LICENSE) provides attribution and upstream-license
	details. WayPOP is not affiliated with Waymo LLC.

	## Splits

	The paper calls the two test splits WayPOP-v1 and WayPOP-XL. Their short IDs are
	used in folder names, manifests, archives, and download commands.

	\| Public name \| Split ID \| Description \|
	\| --- \| --- \| --- \|
	\| Validation \| `val` \| Six Objaverse 1.0 sequence variants with public panoptic annotations. \|
	\| WayPOP-v1 \| `v1` \| Test split with Objaverse 1.0 objects and private annotations. \|
	\| WayPOP-XL \| `xl` \| Test split with Objaverse-XL objects and private annotations. \|

	## Dataset Structure

	The release contains one metadata archive and one archive per sequence variant.
	After extraction, files follow the layout below. Data directories use the split
	IDs defined in [Splits](#splits).

	```text
	waypop/
	├── README.md
	├── FORMAT.md
	├── LICENSE
	├── TERMS_OF_USE.md
	├── categories.json
	├── release.json
	├── sequences.jsonl
	├── SHA256SUMS
	├── assets/
	│ └── teaser.gif
	├── tools/
	│ ├── read_sample.py
	│ └── verify_download.py
	├── val/
	│ ├── images/
	│ ├── pointcloud/
	│ ├── panoptic/
	│ ├── wstq_meta/
	│ └── meta/
	│ ├── samples.jsonl
	│ └── sequences/
	├── v1/
	│ ├── images/
	│ ├── pointcloud/
	│ └── meta/
	└── xl/
	├── images/
	├── pointcloud/
	└── meta/
	```

	### File Organization

	RGB images (`images/`) and panoptic annotations (`panoptic/`) are organized by
	sequence, camera, and frame. Point clouds (`pointcloud/`) are organized by
	sequence and frame. All RGB images are stored as PNG files.

	### Sample Metadata

	Each data directory contains a `meta/samples.jsonl` file with one record per
	camera-frame sample. Each record includes:

	- Identifiers: Sample and sequence identifiers, source sequence, clip, and asset source.
	- Camera and frame information: Camera information, frame index, timestamp, and image dimensions.
	- Data paths: Paths to the corresponding RGB image and point cloud.
	- Poses: Vehicle and image poses.

	Validation records additionally include paths to panoptic annotations and
	wSTQ metadata, together with the panoptic label divisor.

	## Download

	After access to [the WayPOP dataset repository](https://huggingface.co/datasets/mohangrim/WayPOP)
	is granted, download and extract the complete release:

	```bash
	python -m pip install -U huggingface_hub
	hf auth login
	hf download mohangrim/WayPOP --repo-type dataset --local-dir waypop_download
	python waypop_download/reconstruct_release.py waypop_download waypop
	python waypop/tools/verify_download.py waypop
	```

	To download only validation data:

	```python
	from huggingface_hub import snapshot_download

	snapshot_download(
	repo_id="mohangrim/WayPOP",
	repo_type="dataset",
	local_dir="waypop_download",
	allow_patterns=[
	"archive_index.json",
	"reconstruct_release.py",
	"README.md",
	"FORMAT.md",
	"TERMS_OF_USE.md",
	"waypop-2026-metadata.tar",
	"waypop-2026-val-*.tar",
	],
	)
	```

	```bash
	python waypop_download/reconstruct_release.py waypop_download waypop --split val
	python waypop/tools/verify_download.py waypop --split val
	```

	The extraction script checks archive hashes before writing files. Both tools
	also accept `--sequence SEQUENCE_ID` for an individual variant.
	Markdown documentation is copied from the download folder and can be updated
	without changing the checksummed data archives.

	## Reading the Data

	See [FORMAT.md](FORMAT.md) for sample paths, sensor metadata, validation
	annotations, and benchmark prediction and submission formats.

	## Classes

	[`categories.json`](categories.json) is the authoritative taxonomy and
	benchmark category list. Predictions use these raw category IDs:

	\| Raw ID \| Category \| Type \|
	\| ---: \| --- \| --- \|
	\| 2 \| car \| thing \|
	\| 3 \| truck \| thing \|
	\| 4 \| bus \| thing \|
	\| 5 \| other_large_vehicle \| thing \|
	\| 6 \| bicycle \| stuff \|
	\| 7 \| motorcycle \| stuff \|
	\| 8 \| trailer \| thing \|
	\| 9 \| pedestrian \| thing \|
	\| 10 \| cyclist \| thing \|
	\| 11 \| motorcyclist \| stuff \|
	\| 14 \| construction_cone_pole \| stuff \|
	\| 15 \| pole \| stuff \|
	\| 16 \| pedestrian_object \| stuff \|
	\| 17 \| sign \| stuff \|
	\| 18 \| traffic_light \| stuff \|
	\| 19 \| building \| stuff \|
	\| 20 \| road \| stuff \|
	\| 21 \| lane_marker \| stuff \|
	\| 22 \| road_marker \| stuff \|
	\| 23 \| sidewalk \| stuff \|
	\| 24 \| vegetation \| stuff \|
	\| 25 \| sky \| stuff \|
	\| 29 \| unknown \| unknown/OOD \|

	## Citation

	If you use WayPOP in your research, please cite our paper:

	[WayPOP: A Panoramic Open-Set Panoptic Tracking Benchmark](http://arxiv.org/abs/2611.05801)
	(NeurIPS, 2026)

	```bibtex
	@inproceedings{waypop2026,
	title={WayPOP: A Panoramic Open-Set Panoptic Tracking Benchmark},
	author={Mohan, Rohit and Luz, Maximilian and Chowdhury, Swarnava and Lu, Yao and Drews, Florian and N{\"u}rnberg, Thomas and Miron, Yakov and Tombari, Federico and Gasperini, Stefano and Valada, Abhinav},
	booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
	year={2026}
	}
	```

	## Contact

	For dataset questions, bug reports, benchmark submission issues, or
	collaboration inquiries, contact Rohit Mohan at
	[mohan@cs.uni-freiburg.de](mailto:mohan@cs.uni-freiburg.de).