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Synscapes
2D Box
2D Semantic Segmentation
2D Instance Segmentation
2D Cuboid
Depth
Autonomous Driving
|...
License: Unknown

Overview

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 geometric and material composition,
to the optics, sensor and processing of the camera system. Synscapes was created in collaboration
between 7DLabs Inc., and researchers at Linköping University.

25,000 procedural & unique images

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

Physically based rendering, lights and materials

Synscapes was created using the same physically based rendering techniques that power high-end
visual effects in the film industry. Unbiased path tracing
tracks the propagation of light using radiometric properties from the sun and the sky, modeling
its interaction with surfaces using physically based reflectance models, and ensuring that
each image is representative of the real world.

Optical simulation

img

No optical system is perfect, and the effects
of light scattering in a camera's lens can have a large impact on an image's appearance. In
particular, when the sun is visible either directly in view, or through bright specular highlights,
the image's contrast is significantly reduced. Synscapes models this effect using a long-tail
point spread function (PSF).

Sensor simulation and processing

img

As light strikes a digital sensor, photons are converted into
current, and the signal is converted into an image. Synscapes models this process in detail,
providing an accurate representation of the the following:

  • Motion blur is present
    in each image, both due to the speed of the ego vehicle where the camera is mounted, as well
    as that of surrounding vehicles.
  • Auto-exposure ensures that each image is well lit,
    but can also be "tricked" in high contrast scenes, much as a real sensor system could.
  • A 10 bit sensor is simulated, with physically plausible shot noise.
  • The
    simulated sensor output is produced by applying a camera response curve, whose result is
    subsequently quantized to 8 bit PNG-format images.

Multi-dimensional distribution

Synscapes was constructed in such a
way that each parameter varies independently, providing a broad distribution across all dimensions
of variation. As explored further in our white paper,
this property allows for analysis of a neural network's performance by evaluating it selectively
on different parts of the dataset, for example the 10% nearest to sunrise or the 5% with the
narrowest sidewalk.

img img

In the graph above, the dataset is segmented into 10 subsets according
to the sun_height metadata property. Still, each subset exhibits a consistent distribution
across all other dimensions, with ego_speed and sidewalk_width illustrated.

Data annotation

RGB data

<img src="https://synscapes.on.liu.se/img/4595.jpg"
alt="img"style="zoom:33%;"/>

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

The class annotations follows the Cityscapes convention.

img

Instance as PNG

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

img

Depth as floating point OpenEXR

Stores the planar depth (not distance) in meters.

img

Instruction

Dataset Layout

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 pixel noise is present at the appropriate scale.

Camera metadata

The camera's position and field of view is as follows:

"camera": {
    "extrinsic": {
      "pitch": 0.038,
      "roll": -0.0,
      "x": 1.7,
      "y": 0.1,
      "yaw": -0.0195,
      "z": 1.22
    },
    "intrinsic": {
      "fx": 1590.83437,
      "fy": 1592.79032,
      "resx": 1440,
      "resy": 720,
      "u0": 771.31406,
      "v0": 360.79945
    }
  }

Instance metadata

2D Bounding Boxes

img

3D Bounding Boxes in ego-vehicle coordinates

img

Occlusion

(fraction of object hidden behind other objects) img

Truncation (fraction of object outside field of view)

img

Note: 'class' is also recorded in the JSON file, to facilitate
instance-to-class mapping without having to refer to the PNG file.

Scene Metadata

altitude_variation
The largest altitude difference in the scene in meters.

curb_height
The height of the sidewalk curb in meters.

dist***{mean,stddev}
For each actor class,
contains the mean and standard deviation of distance for all visible instances.

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

fence_{presence,height}
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.

median_presence
Whether the road median is present.

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

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

rel_dist_to_isect
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.

road_material_type
Integer representing the material used for the road surface.

sidewalk_width
The width of the sidewalk in meters

sky_contrast
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.

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

wall_{presence,height}
Whether the wall class is present, with height in meters.

Citation

Please use the following citation when referencing the dataset:

@article{wrenninge2018synscapes,
  title={Synscapes: A photorealistic synthetic dataset for street scene parsing},
  author={Wrenninge, Magnus and Unger, Jonas},
  journal={arXiv preprint arXiv:1810.08705},
  year={2018}
}
Data Summary
Type
Image,
Amount
25K
Size
188.39GB
Provided by
7DLabs Inc
The company engages in capturing the effects of everything from illumination by sun and sky, to the scene's geometric and material composition, to the optics, sensor and processing of the camera system.
Annotated by
7DLabs Inc
Provider of photorealistic synthetic data sets for street scenes based in San Francisco, California. The company engages in capturing the effects of everything from illumination by sun and sky, to the scene's geometric and material composition, to the optics, sensor and processing of the camera system.
| Amount 25K | Size 188.39GB
Synscapes
2D Box 2D Semantic Segmentation 2D Instance Segmentation 2D Cuboid Depth
Autonomous Driving
License: Unknown

Overview

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 geometric and material composition,
to the optics, sensor and processing of the camera system. Synscapes was created in collaboration
between 7DLabs Inc., and researchers at Linköping University.

25,000 procedural & unique images

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

Physically based rendering, lights and materials

Synscapes was created using the same physically based rendering techniques that power high-end
visual effects in the film industry. Unbiased path tracing
tracks the propagation of light using radiometric properties from the sun and the sky, modeling
its interaction with surfaces using physically based reflectance models, and ensuring that
each image is representative of the real world.

Optical simulation

img

No optical system is perfect, and the effects
of light scattering in a camera's lens can have a large impact on an image's appearance. In
particular, when the sun is visible either directly in view, or through bright specular highlights,
the image's contrast is significantly reduced. Synscapes models this effect using a long-tail
point spread function (PSF).

Sensor simulation and processing

img

As light strikes a digital sensor, photons are converted into
current, and the signal is converted into an image. Synscapes models this process in detail,
providing an accurate representation of the the following:

  • Motion blur is present
    in each image, both due to the speed of the ego vehicle where the camera is mounted, as well
    as that of surrounding vehicles.
  • Auto-exposure ensures that each image is well lit,
    but can also be "tricked" in high contrast scenes, much as a real sensor system could.
  • A 10 bit sensor is simulated, with physically plausible shot noise.
  • The
    simulated sensor output is produced by applying a camera response curve, whose result is
    subsequently quantized to 8 bit PNG-format images.

Multi-dimensional distribution

Synscapes was constructed in such a
way that each parameter varies independently, providing a broad distribution across all dimensions
of variation. As explored further in our white paper,
this property allows for analysis of a neural network's performance by evaluating it selectively
on different parts of the dataset, for example the 10% nearest to sunrise or the 5% with the
narrowest sidewalk.

img img

In the graph above, the dataset is segmented into 10 subsets according
to the sun_height metadata property. Still, each subset exhibits a consistent distribution
across all other dimensions, with ego_speed and sidewalk_width illustrated.

Data annotation

RGB data

<img src="https://synscapes.on.liu.se/img/4595.jpg"
alt="img"style="zoom:33%;"/>

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

The class annotations follows the Cityscapes convention.

img

Instance as PNG

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

img

Depth as floating point OpenEXR

Stores the planar depth (not distance) in meters.

img

Instruction

Dataset Layout

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 pixel noise is present at the appropriate scale.

Camera metadata

The camera's position and field of view is as follows:

"camera": {
    "extrinsic": {
      "pitch": 0.038,
      "roll": -0.0,
      "x": 1.7,
      "y": 0.1,
      "yaw": -0.0195,
      "z": 1.22
    },
    "intrinsic": {
      "fx": 1590.83437,
      "fy": 1592.79032,
      "resx": 1440,
      "resy": 720,
      "u0": 771.31406,
      "v0": 360.79945
    }
  }

Instance metadata

2D Bounding Boxes

img

3D Bounding Boxes in ego-vehicle coordinates

img

Occlusion

(fraction of object hidden behind other objects) img

Truncation (fraction of object outside field of view)

img

Note: 'class' is also recorded in the JSON file, to facilitate
instance-to-class mapping without having to refer to the PNG file.

Scene Metadata

altitude_variation
The largest altitude difference in the scene in meters.

curb_height
The height of the sidewalk curb in meters.

dist***{mean,stddev}
For each actor class,
contains the mean and standard deviation of distance for all visible instances.

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

fence_{presence,height}
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.

median_presence
Whether the road median is present.

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

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

rel_dist_to_isect
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.

road_material_type
Integer representing the material used for the road surface.

sidewalk_width
The width of the sidewalk in meters

sky_contrast
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.

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

wall_{presence,height}
Whether the wall class is present, with height in meters.

Citation

Please use the following citation when referencing the dataset:

@article{wrenninge2018synscapes,
  title={Synscapes: A photorealistic synthetic dataset for street scene parsing},
  author={Wrenninge, Magnus and Unger, Jonas},
  journal={arXiv preprint arXiv:1810.08705},
  year={2018}
}
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