KITTI

KITTI Vision Benchmark Suite: We take advantage of our autonomous driving platform Annieway to develop novel challenging real-world computer vision benchmarks. Our tasks of interest are: stereo, optical flow, visual odometry, 3D object detection and 3D tracking. For this purpose, 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. Besides providing all data in raw format, we extract benchmarks for each task. For each of our benchmarks, we also provide an evaluation metric and this evaluation website. Preliminary experiments show that methods ranking high on established benchmarks such as Middlebury perform below average when being moved outside the laboratory to the real world. Our goal is to reduce this bias and complement existing benchmarks by providing real-world benchmarks with novel difficulties to the community.


References in zbMATH (referenced in 40 articles )

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  1. Patil A., Malla S., Gang H., Chen Y.-T.: The H3D Dataset for Full-Surround 3D Multi-Object Detection and Tracking in Crowded Urban Scenes (2019) arXiv
  2. Pei Sun, Henrik Kretzschmar, Xerxes Dotiwalla, Aurelien Chouard, Vijaysai Patnaik, Paul Tsui, James Guo, Yin Zhou, Yuning Chai, Benjamin Caine, Vijay Vasudevan, Wei Han, Jiquan Ngiam, Hang Zhao, Aleksei Timofeev, Scott Ettinger, Maxim Krivokon, Amy Gao, Aditya Joshi, Sheng Zhao, Shuyang Cheng, Yu Zhang, Jonathon Shlens, Zhifeng Chen, Dragomir Anguelov: Scalability in Perception for Autonomous Driving: Waymo Open Dataset (2019) arXiv
  3. Pengpeng Liu, Irwin King, Michael R.Lyu, Jia Xu: DDFlow: Learning Optical Flow with Unlabeled Data Distillation (2019) arXiv
  4. Shi, Xinchu; Ling, Haibin; Pang, Yu; Hu, Weiming; Chu, Peng; Xing, Junliang: Rank-1 tensor approximation for high-order association in multi-target tracking (2019)
  5. Lindeberg, Tony: Spatio-temporal scale selection in video data (2018)
  6. Xinyu Huang, Peng Wang, Xinjing Cheng, Dingfu Zhou, Qichuan Geng, Ruigang Yang: The ApolloScape Open Dataset for Autonomous Driving and its Application (2018) arXiv
  7. Berger, Johannes; Lenzen, Frank; Becker, Florian; Neufeld, Andreas; Schnörr, Christoph: Second-order recursive filtering on the rigid-motion Lie group (\mathrmSE_3) based on nonlinear observations (2017)
  8. Burger, Martin; Dirks, Hendrik; Frerking, Lena: On optical flow models for variational motion estimation (2017)
  9. Coninx, Alexandre; Bessière, Pierre; Droulez, Jacques: Quick and energy-efficient Bayesian computing of binocular disparity using stochastic digital signals (2017)
  10. Wang, Shaofei; Fowlkes, Charless C.: Learning optimal parameters for multi-target tracking with contextual interactions (2017)
  11. Hollósi, Gergely; Lukovszki, Csaba; Moldován, István; Plósz, Sándor; Harasztos, Frigyes: Monocular indoor localization techniques for smartphones (2016)
  12. Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, Bernt Schiele: The Cityscapes Dataset for Semantic Urban Scene Understanding (2016) arXiv
  13. Xia, Shengxiang: A topological analysis on patches of optical flow (2016)
  14. Žbontar, Jure; Lecun, Yann: Stereo matching by training a convolutional neural network to compare image patches (2016)
  15. Buades, Antoni; Facciolo, Gabriele: Reliable multiscale and multiwindow stereo matching (2015)
  16. Demetz, Oliver; Hafner, David; Weickert, Joachim: Morphologically invariant matching of structures with the complete rank transform (2015)
  17. Dhiman, Nitin Kumar; Deodhare, Dipti; Khemani, Deepak: \textitWheream I? Creating spatial awareness in unmanned ground robots using SLAM: a survey (2015) ioport
  18. Hafner, David; Demetz, Oliver; Weickert, Joachim; Reißel, Martin: Mathematical foundations and generalisations of the census transform for robust optic flow computation (2015)
  19. Sun, Deqing; Roth, Stefan; Black, Michael J.: A quantitative analysis of current practices in optical flow estimation and the principles behind them (2014) ioport
  20. Becker, Florian; Lenzen, Frank; Kappes, Jörg H.; Schnörr, Christoph: Variational recursive joint estimation of dense scene structure and camera motion from monocular high speed traffic sequences (2013)