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KITTI-semantics
2D Semantic Segmentation
2D Instance Segmentation
Autonomous Driving
|...
License: CC BY-NC-SA 3.0

Overview

This is the KITTI semantic instance segmentation benchmark. It consists of 200 semantically
annotated train as well as 200 test images corresponding to the KITTI Stereo and Flow Benchmark 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{[Alhaija2018IJCV](http://www.cvlibs.net/publications/Alhaija2018IJCV.pdf),
 author = {Hassan Alhaija and Siva Mustikovela and [Lars Mescheder](http://avg.is.tuebingen.mpg.de/person/lmescheder)
and [Andreas Geiger](http://www.cvlibs.net/) and Carsten Rother},
 title = {Augmented Reality
Meets Computer Vision: Efficient Data Generation for Urban Driving Scenes},
 journal = {International Journal of Computer Vision (IJCV)},
 year = {2018}
}

License

CC BY-NC-SA 3.0

Data Summary
Type
Image,
Amount
400
Size
312.69MB
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 400 | Size 312.69MB
KITTI-semantics
2D Semantic Segmentation 2D Instance Segmentation
Autonomous Driving
License: CC BY-NC-SA 3.0

Overview

This is the KITTI semantic instance segmentation benchmark. It consists of 200 semantically
annotated train as well as 200 test images corresponding to the KITTI Stereo and Flow Benchmark 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{[Alhaija2018IJCV](http://www.cvlibs.net/publications/Alhaija2018IJCV.pdf),
 author = {Hassan Alhaija and Siva Mustikovela and [Lars Mescheder](http://avg.is.tuebingen.mpg.de/person/lmescheder)
and [Andreas Geiger](http://www.cvlibs.net/) and Carsten Rother},
 title = {Augmented Reality
Meets Computer Vision: Efficient Data Generation for Urban Driving Scenes},
 journal = {International Journal of Computer Vision (IJCV)},
 year = {2018}
}

License

CC BY-NC-SA 3.0

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