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You must agree to the NVIDIA Autonomous Vehicles NuRec Dataset License
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### NVIDIA Autonomous Vehicles NuRec Dataset License Agreement
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extra_gated_button_content: >-
I accept the terms of the NVIDIA Autonomous Vehicles NuRec Dataset License
Agreement
license: other
license_name: nvidia-av-dataset
license_link: >-
https://huggingface.co/datasets/nvidia/PhysicalAI-Autonomous-Vehicles-NuRec/blob/main/LICENSE.pdf
viewer: false
task_categories: - robotics tags: - physicalAI
🚀 News Update (October 22, 2025 - Many More Scenes and Better Ease of Use)!!
We have now:
- Increased our number of NuRec scenes to 924!!
- Added labels.json file for helping users who want to search by types of scenes based on: behavior, layout, lighting, road types, surface conditions, traffic density, vrus presence, and weather. (Note this is only available for files under Batch0002 and onwards)
- A front camera video file for each clips so that users can assess the scene before opening the usdz. (Note this is only available for files under Batch0002 and onwards)
Find the 900+ scenes in the sample_set/25.07_release folder.
Dataset Description:
Neural reconstructed dataset that carries 3D reconstructed driving scenes. The scenes are about 20 second long and stored in form of usdz files, along with respective xodr map files, surface mesh. The reconstructions were generated using 6 camera views (front-wide 120 deg, front-tele 30 deg, cross right/left 120 deg and rear right/left 70 deg). Users can use these 3D reconstructed driving scenes for training and testing their autonomous vehicle (AV) systems. This dataset is ready for commercial/non-commercial AV only use.
Dataset Owner(s):
NVIDIA Corporation
Dataset Creation Date:
06/09/2025
License/Terms of Use:
NVIDIA Autonomous Vehicle Dataset License Agreement
Intended Usage:
This dataset is intended to provide AV developers with the experience of NuRec capability and try out 3DGUT. Users can use this dataset to run a set of tests / experiments for an AV system and train AI models that use camera and reconstructed data. The scenes in this dataset are generated by and can be rendered by using NVIDIA NuRec. CARLA users can also utilize this dataset by which leveraging the NVIDIA NuRec integration in CARLA.
Dataset Characterization
Data Collection Method
- [Automatic/Sensors] - [Machine-derived]
Labeling Method
- [Automatic/Sensors] - [Machine-derived]
Dataset Format
The scenes are stored by batches containing a number of clip folders listed by their UUIDs. Each UUID folder contains:
- usdz file (always)
- labels.json file (in most cases)
- camera_front_wide_120fov.mp4 (in most cases)
Each reconstructed scene is stored as a USDZ File containing the following:
| Files | Description |
|---|---|
| checkpoint.ckpt | Trained neural network weights |
| data_info.json | Timestamp and frame range detail per sensors |
| datasource_summary.json | Sensor track and poses summary |
| default.usda | Main scene file referencing all assets and configurations |
| dome_light.usda | Describe dome lighting for scene illumination |
| map.xodr | OpenDRIVE map file |
| mesh.ply | Polygon mesh file for 3D geometry |
| mesh.usd | USD file for 3D mesh |
| mesh_ground.ply | Polygon mesh file for ground surface geometry |
| mesh_ground.usd | USD file for ground mesh |
| metadata.yaml | YAML file with scene metadata |
| parsed_config.yaml | YAML configuration file |
| rig_trajectories.json | JSON file containing sensor rig trajectory data |
| rig_trajectories.usda | Rig trajectories in the USD scene |
| sequence_tracks.json | JSON file with object tracking information |
| sequence_tracks.usda | Object sequence tracks in the USD scene |
| volume.nurec | volumetric data file for neural reconstruction |
| volume.usda | USD ASCII file describing volumetric data in the scene |
The labels.json contains the following fields where each field except VRUs can have multiple values:
- Behavior types: {driving_straight, stop, left_lane_change, right_lane_change, right_turn, left_turn, unspecified, reverse}
- Layout types: {straight_road, intersection, underpass, unspecified, bridge, construction_zone, parking_lot, pedestrian_crossing, ramp, roundabout, railway_crossing}
- Road types: {residential, highways, urban, unspecified, rural, other}
- Weather types: {clear/cloudy, unspecified, rain, fog}
- Surface conditions: {dry, unspecified, wet}
- Lighting types: {daytime, unspecified, nighttime}
- VRUs: {True, False}
- Traffic density: {low, medium, high, unspecified}
Dataset Quantification
Record Count: 900+ usdz files (more coming soon)
Measurement of Total Data Storage: approx 4.5 TB
Downloading Data
Please see https://huggingface.co/docs/huggingface_hub/v1.0.0.rc5/en/guides/download for complete documentation on how download dataset files. The code below is just an example only.
from huggingface_hub import login, snapshot_download
def main():
hf_api_token = os.getenv("HF_TOKEN")
login(token=hf_api_token)
# Download an entire repository
snapshot_download(repo_id="nvidia/PhysicalAI-Autonomous-Vehicles-NuRec", repo_type="dataset")
# Download all the files in a folder
snapshot_download(repo_id="nvidia/PhysicalAI-Autonomous-Vehicles-NuRec", repo_type="dataset", allow_patterns="sample_set/25.07_release/Batch0002/001b28cb-b8f7-4627-ae65-fda88612d5bf/*")
# Download an individual file
snapshot_download(repo_id="nvidia/PhysicalAI-Autonomous-Vehicles-NuRec", repo_type="dataset", allow_patterns="sample_set/25.07_release/Batch0002/001b28cb-b8f7-4627-ae65-fda88612d5bf/001b28cb-b8f7-4627-ae65-fda88612d5bf.usdz")
if __name__ == "__main__":
main()
Downloading usdz based upon categories in labels.json
import argparse
from pathlib import Path
import json
import os
from huggingface_hub import login, snapshot_download
def string_to_boolean(s):
s = s.strip().lower() # Normalize the string
if s in ('true', '1', 'yes', 'on'):
return True
return False
def main():
valid_categories = ["behavior", "layout", "lighting", "road_types", "surface_conditions", "traffic_density", "vrus", "weather"]
parser = argparse.ArgumentParser(
description="Downloads usdz clips based upon criteria specified in the labels.json"
)
parser.add_argument(
"--local-dir", type=str, required=True, help="The path to store the usdz"
)
parser.add_argument(
"--category",
type=str,
required=True,
choices=valid_categories,
help="The specified category in the labels.json. Must be one of: %(choices)",
)
parser.add_argument(
"--value",
type=str,
required=True,
help="The specified value in the category",
)
args = parser.parse_args()
hf_api_token = os.getenv("HF_TOKEN")
login(token=hf_api_token)
# First download all the labels.json files
print(f"Downloading dataset labels.json to {args.local_dir}.")
snapshot_download(repo_id="nvidia/PhysicalAI-Autonomous-Vehicles-NuRec", repo_type="dataset", allow_patterns="*.json", local_dir=args.local_dir)
category = args.category
value = args.value
# Find all of the labels.json files that have been downloaded
local_dir = Path(args.local_dir)
label_paths = local_dir.rglob("labels.json")
# Filter through the labels.json and find all usdz that match our criteria
paths_to_download = {}
print(f"Filtering usdz downloads based upon labels.json downloaded with criteria {category} and {value}.")
for label_path in label_paths:
with open(label_path, "r", encoding="utf-8") as f:
metadata = json.load(f)
if category in metadata:
if category == "vrus":
if string_to_boolean(value) == metadata["vrus"]:
paths_to_download[label_path.parent] = True
else:
if value in metadata[args.category]:
paths_to_download[label_path.parent] = True
print(f"Found {len(paths_to_download)} that matched criteria.")
# Download the selected usdz and front camera mp4
for path in paths_to_download.keys():
relative_path = path.relative_to(local_dir)
print(f"Downloading usdz and front camera at path {relative_path}")
snapshot_download(repo_id="nvidia/PhysicalAI-Autonomous-Vehicles-NuRec", repo_type="dataset", allow_patterns=f"{relative_path}/*", local_dir=args.local_dir)
if __name__ == "__main__":
main()
Reference(s):
@article{wu20253dgut, title={3DGUT: Enabling Distorted Cameras and Secondary Rays in Gaussian Splatting}, author={Wu, Qi and Martinez Esturo, Janick and Mirzaei, Ashkan and Moenne-Loccoz, Nicolas and Gojcic, Zan}, journal={Conference on Computer Vision and Pattern Recognition (CVPR)}, year={2025} }
Ethical Considerations:
NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
Please report security vulnerabilities or NVIDIA AI Concerns here.