KITTI-semantics
2D Semantic Segmentation
2D Instance Segmentation
Autonomous Driving
|...
许可协议: 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

数据概要
数据格式
Image,
数据量
400
文件大小
312.69MB
发布方
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
数据集反馈
| 144 | 数据量 400 | 大小 312.69MB
KITTI-semantics
2D Semantic Segmentation 2D Instance Segmentation
Autonomous Driving
许可协议: 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

数据集反馈
0
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