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KITTI-Sceneflow
Optical Flow
Autonomous Driving
|...
License: CC BY-NC-SA 3.0

Overview

The sceneflow 2015 benchmark consists of 200 training scenes and 200 test scenes (4 color images
per scene, saved in loss less png format). It comprises dynamic scenes for which the ground
truth has been established in a semi-automatic process. Our evaluation server computes the
percentage of bad pixels averaged over all ground truth pixels of all 200 test images. For
this benchmark, we consider a pixel to be correctly estimated if the disparity or flow end-point
error is <3px or <5% (for scene flow this criterion needs to be fulfilled for both disparity
maps and the flow map). We require that all methods use the same parameter set for all test
pairs. Our development kit provides details about the data format as well as MATLAB / C++ utility
functions for reading and writing disparity maps and flow fields. More details can be found
in Object Scene Flow for Autonomous Vehicles (CVPR 2015).

Data Collection

We equipped a standard station wagon with two high-resolution color and grayscale video cameras.
Accurate ground truth is provided by a Velodyne laser scanner and a GPS localization system.
Our datsets are captured by driving around the mid-size city of Karlsruhe, in rural areas and
on highways. Up to 15 cars and 30 pedestrians are visible per image.

img

Citation

Please use the following citation when referencing the dataset:

@ARTICLE{Menze2018JPRS,
  author = {Moritz Menze and Christian Heipke and Andreas Geiger},
  title = {Object Scene Flow},
  journal = {ISPRS Journal of Photogrammetry and Remote Sensing (JPRS)},
  year = {2018}
}
@INPROCEEDINGS{Menze2015ISA,
  author = {Moritz Menze and Christian Heipke and Andreas Geiger},
  title = {Joint 3D Estimation of Vehicles and Scene Flow},
  booktitle = {ISPRS Workshop on Image Sequence Analysis (ISA)},
  year = {2015}
}

License

CC BY-NC-SA 3.0

Data Summary
Type
Image,
Amount
1.6K
Size
18.12GB
Provided by
Max Planck Institute for Intellgent Systems
Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems
| Amount 1.6K | Size 18.12GB
KITTI-Sceneflow
Optical Flow
Autonomous Driving
License: CC BY-NC-SA 3.0

Overview

The sceneflow 2015 benchmark consists of 200 training scenes and 200 test scenes (4 color images
per scene, saved in loss less png format). It comprises dynamic scenes for which the ground
truth has been established in a semi-automatic process. Our evaluation server computes the
percentage of bad pixels averaged over all ground truth pixels of all 200 test images. For
this benchmark, we consider a pixel to be correctly estimated if the disparity or flow end-point
error is <3px or <5% (for scene flow this criterion needs to be fulfilled for both disparity
maps and the flow map). We require that all methods use the same parameter set for all test
pairs. Our development kit provides details about the data format as well as MATLAB / C++ utility
functions for reading and writing disparity maps and flow fields. More details can be found
in Object Scene Flow for Autonomous Vehicles (CVPR 2015).

Data Collection

We equipped a standard station wagon with two high-resolution color and grayscale video cameras.
Accurate ground truth is provided by a Velodyne laser scanner and a GPS localization system.
Our datsets are captured by driving around the mid-size city of Karlsruhe, in rural areas and
on highways. Up to 15 cars and 30 pedestrians are visible per image.

img

Citation

Please use the following citation when referencing the dataset:

@ARTICLE{Menze2018JPRS,
  author = {Moritz Menze and Christian Heipke and Andreas Geiger},
  title = {Object Scene Flow},
  journal = {ISPRS Journal of Photogrammetry and Remote Sensing (JPRS)},
  year = {2018}
}
@INPROCEEDINGS{Menze2015ISA,
  author = {Moritz Menze and Christian Heipke and Andreas Geiger},
  title = {Joint 3D Estimation of Vehicles and Scene Flow},
  booktitle = {ISPRS Workshop on Image Sequence Analysis (ISA)},
  year = {2015}
}

License

CC BY-NC-SA 3.0

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