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KITTI-stereo-2015
Disparity
Autonomous Driving
|...
License: CC BY-NC-SA 3.0

Overview

The stero 2015 benchmark consists of 200 training scenes and 200 test scenes (4 color images
per scene, saved in loss less png format). Compared to the stereo 2012 and flow 2012 benchmarks,
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-stereo-2015
Disparity
Autonomous Driving
License: CC BY-NC-SA 3.0

Overview

The stero 2015 benchmark consists of 200 training scenes and 200 test scenes (4 color images
per scene, saved in loss less png format). Compared to the stereo 2012 and flow 2012 benchmarks,
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

0
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