Package: nuscenes-devkit
nuScenes™ devkitWelcome to the nuTonomy® downloadable driverless vehicle software page. Click on the green box above labeled "Code" to download a copy of the software described below.OverviewChangelogDevkit setupnuImagesnuImages setupGetting started with nuImagesnuScenesnuScenes setupPanoptic nuScenesnuScenes-lidarsegPrediction challengeCAN bus expansionMap expansionMap versionsGetting started with nuScenesKnown issuesCitationChangelogFeb. 13, 2023: Devkit v1.1.10: Specify version for various pip requirements.Sep. 20, 2021: Devkit v1.1.9: Refactor tracking eval code for custom datasets with different classes.Sep. 17, 2021: Devkit v1.1.8: Add PAT metric to Panoptic nuScenes.Aug. 23, 2021: Devkit v1.1.7: Add more panoptic tracking metrics to Panoptic nuScenes code.Jul. 29, 2021: Devkit v1.1.6: Panoptic nuScenes v1.0 code, NeurIPS challenge announcement.Apr. 5, 2021: Devkit v1.1.3: Bug fixes and pip requirements.Nov. 23, 2020: Devkit v1.1.2: Release map-expansion v1.3 with lidar basemap.Nov. 9, 2020: Devkit v1.1.1: Lidarseg evaluation code, NeurIPS challenge announcement.Aug. 31, 2020: Devkit v1.1.0: nuImages v1.0 and nuScenes-lidarseg v1.0 code release.Jul. 7, 2020: Devkit v1.0.9: Misc updates on map and prediction code.Apr. 30, 2020: nuImages v0.1 code release.Apr. 1, 2020: Devkit v1.0.8: Relax pip requirements and reorganize prediction code.Mar. 24, 2020: Devkit v1.0.7: nuScenes prediction challenge code released.Feb. 12, 2020: Devkit v1.0.6: CAN bus expansion released.Dec. 11, 2019: Devkit v1.0.5: Remove weight factor from AMOTA tracking metrics.Nov. 1, 2019: Tracking eval code released and detection eval code reorganized.Jul. 1, 2019: Map expansion released.Apr. 30, 2019: Devkit v1.0.1: loosen PIP requirements, refine detection challenge, export 2d annotation script.Mar. 26, 2019: Full dataset, paper, & devkit v1.0.0 released. Support dropped for teaser data.Dec. 20, 2018: Initial evaluation code released. Devkit folders restructured, which breaks backward compatibility.Nov. 21, 2018: RADAR filtering and multi sweep aggregation.Oct. 4, 2018: Code to parse RADAR data released.Sep. 12, 2018: Devkit for teaser dataset released.Devkit setupWe use a common devkit for nuScenes and nuImages.
The devkit is tested for Python 3.6 and Python 3.7.
To install Python, please checkhere.Our devkit is available and can be installed viapip:pip install nuscenes-devkitFor an advanced installation, seeinstallationfor detailed instructions.nuImagesnuImages is a stand-alone large-scale image dataset.
It uses the same sensor setup as the 3d nuScenes dataset.
The structure is similar to nuScenes and both use the same devkit, which make the installation process simple.nuImages setupTo download nuImages you need to go to theDownload page,
create an account and agree to the nuScenesTerms of Use.
For the devkit to work you will need to downloadat least the metadata and samples, thesweepsare optional.
Please unpack the archives to the/data/sets/nuimagesfolder *without* overwriting folders that occur in multiple archives.
Eventually you should have the following folder structure:/data/sets/nuimages
    samples	-	Sensor data for keyframes (annotated images).
    sweeps  -   Sensor data for intermediate frames (unannotated images).
    v1.0-*	-	JSON tables that include all the meta data and annotations. Each split (train, val, test, mini) is provided in a separate folder.If you want to use another folder, specify thedatarootparameter of the NuImages class (see tutorial).Getting started with nuImagesPlease follow these steps to make yourself familiar with the nuImages dataset:Get thenuscenes-devkit code.Run the tutorial using:jupyter notebook $HOME/nuscenes-devkit/python-sdk/tutorials/nuimages_tutorial.ipynbSee thedatabase schemaandannotator instructions.nuScenesnuScenes setupTo download nuScenes you need to go to theDownload page,
create an account and agree to the nuScenesTerms of Use.
After logging in you will see multiple archives.
For the devkit to work you will need to downloadallarchives.
Please unpack the archives to the/data/sets/nuscenesfolder *without* overwriting folders that occur in multiple archives.
Eventually you should have the following folder structure:/data/sets/nuscenes
    samples	-	Sensor data for keyframes.
    sweeps	-	Sensor data for intermediate frames.
    maps	-	Folder for all map files: rasterized .png images and vectorized .json files.
    v1.0-*	-	JSON tables that include all the meta data and annotations. Each split (trainval, test, mini) is provided in a separate folder.If you want to use another folder, specify thedatarootparameter of the NuScenes class (see tutorial).Panoptic nuScenesIn August 2021 we publishedPanoptic nuSceneswhich contains the panoptic labels
of the point clouds for the approximately 40,000 keyframes in nuScenes.
To install Panoptic nuScenes, please follow these steps:Download the dataset from theDownload page,Extract thepanopticandv1.0-*folders to your nuScenes root directory (e.g./data/sets/nuscenes/panoptic,/data/sets/nuscenes/v1.0-*).Get the latest version of the nuscenes-devkit.Get started with thetutorial.nuScenes-lidarsegIn August 2020 we publishednuScenes-lidarsegwhich contains the semantic labels of the point clouds for the approximately 40,000 keyframes in nuScenes.
To install nuScenes-lidarseg, please follow these steps:Download the dataset from theDownload page,Extract thelidarsegandv1.0-*folders to your nuScenes root directory (e.g./data/sets/nuscenes/lidarseg,/data/sets/nuscenes/v1.0-*).Get the latest version of the nuscenes-devkit.If you already have a previous version of the devkit, update the pip requirements (seedetails):pip install -r setup/requirements.txtGet started with thetutorial.Prediction challengeIn March 2020 we released code for the nuScenes prediction challenge.
To get started:Download the version 1.2 of the map expansion (see below).Download the trajectory sets forCoverNetfromhere.Go through theprediction tutorial.For information on how submissions will be scored, visit the challengewebsite.CAN bus expansionIn February 2020 we published the CAN bus expansion.
It contains low-level vehicle data about the vehicle route, IMU, pose, steering angle feedback, battery, brakes, gear position, signals, wheel speeds, throttle, torque, solar sensors, odometry and more.
To install this expansion, please follow these steps:Download the expansion from theDownload page,Extract the can_bus folder to your nuScenes root directory (e.g./data/sets/nuscenes/can_bus).Get the latest version of the nuscenes-devkit.If you already have a previous version of the devkit, update the pip requirements (seedetails):pip install -r setup/requirements.txtGet started with theCAN bus readmeortutorial.Map expansionIn July 2019 we published a map expansion with 11 semantic layers (crosswalk, sidewalk, traffic lights, stop lines, lanes, etc.).
To install this expansion, please follow these steps:Download the expansion from theDownload page,Extract the contents (foldersbasemap,expansionandprediction) to your nuScenesmapsfolder.Get the latest version of the nuscenes-devkit.If you already have a previous version of the devkit, update the pip requirements (seedetails):pip install -r setup/requirements.txtGet started with themap expansion tutorial.
For more information, see themap versionsbelow.Map versionsHere we give a brief overview of the different map versions:v1.3: Add BitMap class that supports new lidar basemap and legacy semantic prior map. Removeone broken lane.v1.2: Expand devkit and maps to include arcline paths and lane connectivity for the prediction challenge.v1.1: Resolved issues with ego poses being off the drivable surface.v1.0: Initial map expansion release from July 2019. Supports 11 semantic layers.nuScenes v1.0: Came with a bitmap for the semantic prior. All code is contained in nuscenes.py.Getting started with nuScenesPlease follow these steps to make yourself familiar with the nuScenes dataset:Read thedataset description.Explorethe lidar viewer and videos.Downloadthe dataset.Get thenuscenes-devkit code.Read theonline tutorialor run it yourself using:jupyter notebook $HOME/nuscenes-devkit/python-sdk/tutorials/nuscenes_tutorial.ipynbRead thenuScenes paperfor a detailed analysis of the dataset.Run themap expansion tutorial.Take a look at theexperimental scripts.For instructions related to the object detection task (results format, classes and evaluation metrics), please refer tothis readme.See thedatabase schemaandannotator instructions.See theFAQs.Known issuesGreat care has been taken to collate the nuScenes dataset and many users have praised the quality of the data and annotations.
However, some minor issues remain:Maps:Forsingapore-hollandvillageandsingapore-queenstownthe traffic light 3d poses are all 0 (except for tz).Forboston-seaport, the ego poses of 3 scenes (499, 515, 517) are slightly incorrect and 2 scenes (501, 502) are outside the annotated area.Forsingapore-onenorth, the ego poses of about 10 scenes were off the drivable surface. This has beenresolved in map v1.1.Some lanes are disconnected from the rest of the lanes. We chose to keep these as they still provide valuable information.Annotations:A small number of 3d bounding boxes is annotated despite the object being temporarily occluded. For this reason we make sure tofilter objects without lidar or radar pointsin the nuScenes benchmarks. Seeissue 366.CitationPlease use the following citation when referencingnuScenes or nuImages:@article{nuscenes2019,
  title={nuScenes: A multimodal dataset for autonomous driving},
  author={Holger Caesar and Varun Bankiti and Alex H. Lang and Sourabh Vora and 
          Venice Erin Liong and Qiang Xu and Anush Krishnan and Yu Pan and 
          Giancarlo Baldan and Oscar Beijbom},
  journal={arXiv preprint arXiv:1903.11027},
  year={2019}
}Please use the following citation when referencingPanoptic nuScenes or nuScenes-lidarseg:@article{fong2021panoptic,
  title={Panoptic nuScenes: A Large-Scale Benchmark for LiDAR Panoptic Segmentation and Tracking},
  author={Fong, Whye Kit and Mohan, Rohit and Hurtado, Juana Valeria and Zhou, Lubing and Caesar, Holger and
          Beijbom, Oscar and Valada, Abhinav},
  journal={arXiv preprint arXiv:2109.03805},
  year={2021}
}