A photorealistic synthetic dataset for street scene parsing.


REALISM From sunlight to sensor

SynScapes is created with an end-to-end approach to realism, accurately capturing the effects of everything from illumination by sun and sky, to the scene's geometry and material composition, to the optics, sensor and processing of the camera system.

25,000 unique images

The images in the SynScapes dataset do not follow a driven path through a single virtual world. Instead, an entirely unique scene is used for each of the twenty-five thousand images. As a result, the dataset contains a wide range of variations and unique combinations of all its features.

multi-dimensional distribution


physically based lights, materials and rendering

Lit by 

Optical simulation

No optical system is perfect, and the effects of light scattering in a camera's lens affects 

sensor simulation

Motion blur. Autoexposure. De-Bayering. Noise modeling. Sensor processing.


annotations and metadata

How many cars are visible in a given image? Is the sky clear or cloudy? SynScapes provides a wide range of metadata which helps characterize each image.

Additionally, SynScapes offers information that is difficult for humans to annotate, such as accurate per-instance visibility.


dataset DETAILS


SynScapes is organized into the following directories:

├── img
│   ├── class     [1-25000].png
│   ├── depth     [1-25000].exr
│   ├── instance  [1-25000].png
│   ├── rgb       [1-25000].png
│   └── rgb-2k    [1-25000].png
└── meta          [1-25000].json

Image resolution

SynScapes' native resolution is 1440x720, stored in the img/rgb folder. In order to best support training with architectures designed for Cityscapes, we also include an up-scaled version at 2048x1024 resolution in img/rgb-2k. Note that this up-scaling precedes the sensor simulation stage, ensuring that de-Bayering and pixel noise is present at the appropriate scale.




  Class  as single-channel PNG (visualized in color above)  The class annotations follows  the Cityscapes convention . 

Class as single-channel PNG (visualized in color above)

The class annotations follows the Cityscapes convention

  Instance  as PNG  The instance id can be found as  R + G * 256 + B * 256^2 .

Instance as PNG

The instance id can be found as R + G * 256 + B * 256^2.

  Depth  as floating point OpenEXR  Stores the planar depth (not distance) in meters.

Depth as floating point OpenEXR

Stores the planar depth (not distance) in meters.


Camera metadata


Instance metadata

scene metadata


The largest altitude difference in the scene in meters.

The height of the sidewalk curb in meters.

For each actor class, contains the mean and standard deviation of distance for all visible instances.

The speed in m/s traveled by the ego vehicle at the time of image capture.

Indicates whether fences are present in the image. Note that due to occlusion, it may be hidden behind another object. Height is measured in meters.

Whether the road median is present.

For each actor class, contains the number of visible instances.

Whether a parking lane is present, and whether cars park at 0 (parallel), 45 or 90 degrees.


Relative distance to nearest intersection. 0.0 indicates ego vehicle is inside the intersection, 1.0 indicates it is one city block away from the next intersection.

Integer representing the material used for the road surface.

The width of the sidewalk in meters

Contains the logarithm of the sky's contrast, measured as max/mean. Values around 2-3 indicate fully overcast sky, 5-6 indicate direct sunlight.

The normalized angular height of the sun. 0.0 indicates sunset/sunrise, 1.0 indicates zenith.

Whether the wall class is present, with height in meters.

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The SynScapes dataset is provided free of charge to academic and non-academic entities to support work such as research, experimentation, scientific publication and teaching.

Upon accepting your request (made by email to, we will send a link where you may download our dataset. We grant you a non-exclusive, non-transferable, non-sublicensable, worldwide license to use the dataset for non-commercial purposes. By requesting any dataset from us, you agree to the following terms of this license:

  1. You may use the dataset for non-commercial purposes only, including research and educational purposes that are not intended to procure commercial gain.
  2. You are prohibited from distributing, reproducing, displaying, editing, altering, modifying, or creating derivative works of the dataset. Information or research that uses the dataset, but does not reproduce it, is permitted, provided it complies with the other terms of this license.
  3. Although we endeavor to create accurate data, all data is provided “as is” and without any express or implied warranties. We hereby disclaim all representations and warranties concerning the validity, scope, accuracy, completeness, safety, or usefulness for any purpose of the licensed content, including all implied warranties of merchantability, fitness for a particular purpose, or otherwise.
  4. You must cite 7D Labs in any work that makes use of the dataset by using the following citation: citation example.
  5. We do not have any liability whatsoever to you or any other person arising out of or related to your procurement or use of the dataset.
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