Datasets:

bertaveira
/

R3D3

---
license: other
license_name: waymo-dataset-license-non-commercial
license_link: https://huggingface.co/datasets/bertaveira/R3D3/blob/main/LICENSE.txt
task_categories:
- image-to-image
tags:
- r3d3
- r3d2
- autonomous-driving
- arxiv:2506.07826
extra_gated_heading: Request research access to R3D3
extra_gated_description: Requests are reviewed individually. Register with Waymo and
  accept its dataset terms before applying. Describe your research and intended use.
extra_gated_prompt: Confirm your existing Waymo registration and acceptance at https://waymo.com/open/terms/.
  Submitting this form does not register you with Waymo. Your responses are used to
  assess your access request.
extra_gated_button_content: Submit access request
extra_gated_fields:
  Full name: text
  Institution or lab: text
  Institutional email: text
  Email used to register with Waymo: text
  Describe your research project and intended use of R3D3: text
  I have registered on the Waymo Open Dataset website and accepted the Waymo Dataset License Agreement: checkbox
  I will use R3D3 in compliance with the Waymo agreement, including its non-commercial restrictions: checkbox
---
# R3D3

Paired images for [R3D2](https://arxiv.org/abs/2506.07826): **29,246 training pairs**
and **9,091 validation pairs**, approximately **74 GB**. FRONT camera only, with
blacklisted sequences excluded. Original 1920×1280 JPEGs are preserved unchanged.

**Non-commercial use only.** R3D3 was made using the Waymo Open Dataset, provided by
Waymo LLC under the [Waymo Dataset License Agreement for Non-Commercial Use](https://waymo.com/open/terms/),
and your access and use of this work are governed by its terms and conditions.
Redistribute only to recipients registered with Waymo who have accepted its terms,
subject to the agreement's other conditions. The complete agreement is in [LICENSE.txt](LICENSE.txt).

## Access

Access is restricted and manually approved case by case to support reproduction,
direct comparisons with R3D2, and related non-commercial research. Approval is not
guaranteed. Please provide your details and describe your intended use in the
request form. Each researcher needing access should submit their own request;
do not share access tokens or credentials.

You must already have registered with Waymo and accepted its dataset terms.
All use and further sharing remain subject to the Waymo agreement.

## Download

After approval, authenticate and download the dataset folder:

```bash
pip install huggingface_hub
hf auth login
hf download bertaveira/R3D3 --repo-type dataset --local-dir R3D3
cd R3D3
sha256sum -c SHA256SUMS
```

This downloads the original JPEG pairs and metadata directly, approximately 74 GB.
No archive extraction is required. Then set the local path as shown below.

## Format

`metadata.json` contains `root`, `train`, and `val`. Each sample has:

- `wo_shadow`: input image path, relative to `root`.
- `gt`: target image path, relative to `root`.
- `seq`: sequence ID.
- `crops`: actor boxes `[center_x, center_y, width, height]` in original image pixels.

Images are under `images/<sequence>/input/` and `images/<sequence>/target/`.
For actor metrics after the 1920×1080 center crop, subtract 100 from each box's
center y and clip to image bounds. The training loader crops and normalizes the
images automatically.

## Use

After extracting, run this once inside the dataset folder to set its local path:

```bash
python - <<'PYTHON'
import json
from pathlib import Path
p = Path("metadata.json")
metadata = json.loads(p.read_text())
metadata["root"] = str(Path.cwd())
p.write_text(json.dumps(metadata, indent=2) + "\n")
PYTHON
```

Then, inside the [R3D2 code repository](https://github.com/zenseact/R3D2), after `uv sync`:

```bash
uv run r3d2/scripts/train.py R3D2 --data.data-path /path/to/R3D3
```

To check the transfer, run `sha256sum -c SHA256SUMS` inside the dataset folder.
The path-configurable `metadata.json` is excluded from the checksum list.

	---
	license: other
	license_name: waymo-dataset-license-non-commercial
	license_link: https://huggingface.co/datasets/bertaveira/R3D3/blob/main/LICENSE.txt
	task_categories:
	- image-to-image
	tags:
	- r3d3
	- r3d2
	- autonomous-driving
	- arxiv:2506.07826
	extra_gated_heading: Request research access to R3D3
	extra_gated_description: Requests are reviewed individually. Register with Waymo and
	accept its dataset terms before applying. Describe your research and intended use.
	extra_gated_prompt: Confirm your existing Waymo registration and acceptance at https://waymo.com/open/terms/.
	Submitting this form does not register you with Waymo. Your responses are used to
	assess your access request.
	extra_gated_button_content: Submit access request
	extra_gated_fields:
	Full name: text
	Institution or lab: text
	Institutional email: text
	Email used to register with Waymo: text
	Describe your research project and intended use of R3D3: text
	I have registered on the Waymo Open Dataset website and accepted the Waymo Dataset License Agreement: checkbox
	I will use R3D3 in compliance with the Waymo agreement, including its non-commercial restrictions: checkbox
	---

	# R3D3

	Paired images for [R3D2](https://arxiv.org/abs/2506.07826): 29,246 training pairs
	and 9,091 validation pairs, approximately 74 GB. FRONT camera only, with
	blacklisted sequences excluded. Original 1920×1280 JPEGs are preserved unchanged.

	Non-commercial use only. R3D3 was made using the Waymo Open Dataset, provided by
	Waymo LLC under the [Waymo Dataset License Agreement for Non-Commercial Use](https://waymo.com/open/terms/),
	and your access and use of this work are governed by its terms and conditions.
	Redistribute only to recipients registered with Waymo who have accepted its terms,
	subject to the agreement's other conditions. The complete agreement is in [LICENSE.txt](LICENSE.txt).

	## Access

	Access is restricted and manually approved case by case to support reproduction,
	direct comparisons with R3D2, and related non-commercial research. Approval is not
	guaranteed. Please provide your details and describe your intended use in the
	request form. Each researcher needing access should submit their own request;
	do not share access tokens or credentials.

	You must already have registered with Waymo and accepted its dataset terms.
	All use and further sharing remain subject to the Waymo agreement.

	## Download

	After approval, authenticate and download the dataset folder:

	```bash
	pip install huggingface_hub
	hf auth login
	hf download bertaveira/R3D3 --repo-type dataset --local-dir R3D3
	cd R3D3
	sha256sum -c SHA256SUMS
	```

	This downloads the original JPEG pairs and metadata directly, approximately 74 GB.
	No archive extraction is required. Then set the local path as shown below.

	## Format

	`metadata.json` contains `root`, `train`, and `val`. Each sample has:

	- `wo_shadow`: input image path, relative to `root`.
	- `gt`: target image path, relative to `root`.
	- `seq`: sequence ID.
	- `crops`: actor boxes `[center_x, center_y, width, height]` in original image pixels.

	Images are under `images/<sequence>/input/` and `images/<sequence>/target/`.
	For actor metrics after the 1920×1080 center crop, subtract 100 from each box's
	center y and clip to image bounds. The training loader crops and normalizes the
	images automatically.

	## Use

	After extracting, run this once inside the dataset folder to set its local path:

	```bash
	python - <<'PYTHON'
	import json
	from pathlib import Path
	p = Path("metadata.json")
	metadata = json.loads(p.read_text())
	metadata["root"] = str(Path.cwd())
	p.write_text(json.dumps(metadata, indent=2) + "\n")
	PYTHON
	```

	Then, inside the [R3D2 code repository](https://github.com/zenseact/R3D2), after `uv sync`:

	```bash
	uv run r3d2/scripts/train.py R3D2 --data.data-path /path/to/R3D3
	```

	To check the transfer, run `sha256sum -c SHA256SUMS` inside the dataset folder.
	The path-configurable `metadata.json` is excluded from the checksum list.