When using this dataset in your research, we will be happy if you cite us! (or bring us some self-made cake or ice-cream)
For the stereo 2012, flow 2012, odometry, object detection or tracking benchmarks, please cite:
@INPROCEEDINGS{Geiger2012CVPR,
author = {Andreas Geiger and Philip Lenz and Raquel Urtasun},
title = {Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite},
booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2012}
}
For the raw dataset, please cite:
@ARTICLE{Geiger2013IJRR,
author = {Andreas Geiger and Philip Lenz and Christoph Stiller and Raquel Urtasun},
title = {Vision meets Robotics: The KITTI Dataset},
journal = {International Journal of Robotics Research (IJRR)},
year = {2013}
}
For the road benchmark, please cite:
@INPROCEEDINGS{Fritsch2013ITSC,
author = {Jannik Fritsch and Tobias Kuehnl and Andreas Geiger},
title = {A New Performance Measure and evaluation Benchmark for Road Detection Algorithms},
booktitle = {International Conference on Intelligent Transportation Systems (ITSC)},
year = {2013}
}
For the stereo 2015, flow 2015 and scene flow 2015 benchmarks, please cite:
@INPROCEEDINGS{Menze2015CVPR,
author = {Moritz Menze and Andreas Geiger},
title = {Object Scene Flow for Autonomous Vehicles},
booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2015}
}
资源介绍
KITTI 是一套 计算机视觉 算法 评测数据集，其主要用于自动驾驶场景下的相关测试，评测种类涵盖立体图像、光流、视觉测距、3D 物体检测和 3D 追踪等。KITTI 包含市区、乡村和高速公路等场景采集的真实图像数据，每张图像有最多 15 辆车和 30 个行人，并且拥有不同程度的遮挡和截断。
该数据集由 389 对立体图像和光流图、39.2km 视觉测距序列以及超过 200k 个 3D 标注物体图像组成，并以 10Hz 采样同步，其中原始数据集被分为「Road」、「City」、「Residential」、「Campus」和「Person」五类，而 3D 物体检测则分为 car、van、truck、pedestrian、 pedestrian(sitting)、cyclist、tram 以及 misc。
KITTI 数据集由德国卡尔斯鲁厄理工学院和丰田美国技术研究院于 2013 年联合发布，相关论文有《Vision meet Robotics：The KITTI Dataset》。
Citation
Privacy
This dataset is made available for academic use only. However, we take your privacy seriously! If you find yourself or personal belongings in this dataset and feel unwell about it, please contact us and we will immediately remove the respective data from our server.
Credits
We thank Karlsruhe Institute of Technology (KIT) and Toyota Technological Institute at Chicago (TTI-C) for funding this project and Jan Cech (CTU) and Pablo Fernandez Alcantarilla (UoA) for providing initial results. We further thank our 3D object labeling task force for doing such a great job: Blasius Forreiter, Michael Ranjbar, Bernhard Schuster, Chen Guo, Arne Dersein, Judith Zinsser, Michael Kroeck, Jasmin Mueller, Bernd Glomb, Jana Scherbarth, Christoph Lohr, Dominik Wewers, Roman Ungefuk, Marvin Lossa, Linda Makni, Hans Christian Mueller, Georgi Kolev, Viet Duc Cao, Bünyamin Sener, Julia Krieg, Mohamed Chanchiri, Anika Stiller. Many thanks also to Qianli Liao (NYU) for helping us in getting the don't care regions of the object detection benchmark correct. Special thanks for providing the voice to our video go to Anja Geiger!
