Cirrus dataset

Volvo Cars’ new platform for innovation

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A curated dataset with unique long range LiDAR point clouds and scanning patterns, shared to aid advancements in machine perception for safe self-driving technology.

To date, LiDAR research has relied on standard range point clouds and uniform scanning patterns. With the release of Cirrus, we provide a non-uniform distribution of LiDAR scanning patterns with emphasis on long range. Cirrus also includes corresponding camera images, uniform scanning patterns and annotations.

We hope this will encourage research in algorithm development for long range LiDAR detection and classification.

“Mackerel sky and mares’ tails
make tall ships carry low sails.”

High-altitude Cirrus clouds have historically helped sailors prepare for tougher conditions, well in advance.
Cirrus LiDAR range comparison


250 meters


120 meters


70 meters

Seconds in advance

Highway, 120km/h


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The Cirrus dataset contains 6,285 pairs of RGB, LiDAR Gaussian, and LiDAR Uniform frames. Cirrus has been annotated for eight object categories (described below) across the entire 250-meter LiDAR effective range. It includes both high-speed highway and low-speed urban-road scenarios are included. All images have gone through an anonymization process blurring faces and license plates to eliminate personally identifiable information.


The following sensors were used to collect the Cirrus dataset:

  • RGB camera with a resolution of 1920 × 650.
  • 2xLuminar Hydra LiDAR Sensors: 10Hz, 64 lines per frame, 1550-nm, 250m effective range, > 200 meters range to 10% reflective target (Lambertian), 120° horizontal FOV, 30° vertical FOV.
  • 2xGPS and IMU with a resolution of 1920 × 650.


Cirrus contains the followingannotated object categories:

  • 8 categories of objects: Vehicle, Large Vehicle, Pedestrian, Bicycle, Animal, Wheeled Pedestrian, Motorcycle, Trailer.


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Each of the following examples from Cirrus is taken on Highway, and includes Camera image (.jpg), LiDAR-Gaussian (.xyz), LiDAR-Uniform (.xyz), Annotation (.json).

Five zip files. Total 5 Scenes, 6.4 MB.

Download Cirrus

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Each file below includes Camera images (.jpg) for reference, LiDAR-Gaussian (.xyz), LiDAR-Uniform (.xyz), Annotation (.json) for the point clouds as well as a matching file (.txt).

Use of the matching file:

  1. Provided Annotations are performed on the Gaussian data.
  2. Image frames and Gaussian frames share the same timestamp for 1-to-1 mapping with the Annotation files.
  3. A Uniform frame is matched with a Gaussian frame timestamp by using match_uniform*.txt file, where Column 1 = Gaussian file name and Column 2 = Uniform file name.
Seven zip files. Total 6285 Scenes, 9.8 GB.
Legal Notices:
  1. Luminar Technologies, Inc. is the sole and exclusive owner of this dataset.
  2. The dataset is licensed under CC BY-SA 4.0.
  3. Any public use, distribution, display of this data set must contain this notice in its entirety.

Volvo Cars takes reasonable care to remove or hide personal data including faces of people and license plates of vehicles.

If you would like us to modify or remove certain images from the Cirrus dataset, please contact


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When using the Cirrus dataset for public distribution please cite the following:

  title = {Range adaptation for 3d object detection in lidar},
  author = {Wang, Ze and Ding, Sihao and Li, Ying and Zhao, Minming and Roychowdhury, Sohini and Wallin, Andreas and Sapiro, Guillermo and Qiu, Qiang},
  booktitle = {Proceedings of the IEEE International Conference on Computer Vision Workshops},
  year = {2019}
  title = {Cirrus: A Long-range Bi-pattern LiDAR Dataset},
  author = {Wang, Ze and Ding, Sihao and Li, Ying and Zhao, Minming and Roychowdhury, Sohini and Wallin, Andreas and Fenn, Jonas and Sapiro, Guillermo and Qiu, Qiang and Martin, Lane and Ryvola, Scott},
  website = {\url{}},
  year = {2020}
  title = {Cirrus Dataset},
  website = {\url{}},
  copyright = {Luminar Technologies, Inc.},
  license = {CC BY-SA 4.0},
  year = {2020}
Copyright © 2021 Volvo Car Corporation (or its affiliates or licensors)