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 29 articles )

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  1. Marcos Nieto, Orti Senderos, Oihana Otaegui: Boosting AI applications: Labeling format for complex datasets (2021) not zbMATH
  2. Mark Weber, Huiyu Wang, Siyuan Qiao, Jun Xie, Maxwell D. Collins, Yukun Zhu, Liangzhe Yuan, Dahun Kim, Qihang Yu, Daniel Cremers, Laura Leal-Taixe, Alan L. Yuille, Florian Schroff, Hartwig Adam, Liang-Chieh Chen: DeepLab2: A TensorFlow Library for Deep Labeling (2021) arXiv
  3. Wu, Zhenni; Chen, Hengxin; Fang, Bin; Li, Zihao; Chen, Xinrun: Building pose estimation from the perspective of UAVs based on CNNs (2021)
  4. Zeng, Chao; Ng, Michael K.: Incremental CP tensor decomposition by alternating minimization method (2021)
  5. Zöller, Marc-André; Huber, Marco F.: Benchmark and survey of automated machine learning frameworks (2021)
  6. Bredies, Kristian; Holler, Martin: Higher-order total variation approaches and generalisations (2020)
  7. Qin, Zixuan; Yin, Mengxiao; Li, Guiqing; Yang, Feng: SP-Flow: self-supervised optical flow correspondence point prediction for real-time SLAM (2020)
  8. Datta, Amitava; Kaur, Amardeep; Lauer, Tobias; Chabbouh, Sami: Exploiting multi-core and many-core parallelism for subspace clustering (2019)
  9. Holger Caesar, Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, Oscar Beijbom: nuScenes: A multimodal dataset for autonomous driving (2019) arXiv
  10. Jens Behley, Martin Garbade, Andres Milioto, Jan Quenzel, Sven Behnke, Cyrill Stachniss, Juergen Gall: SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences (2019) arXiv
  11. Naiel, Mohamed A.; Ahmad, M. Omair; Swamy, M. N. S.: A vehicle detection scheme based on two-dimensional HOG features in the DFT and DCT domains (2019)
  12. 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
  13. 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
  14. Pengpeng Liu, Irwin King, Michael R.Lyu, Jia Xu: DDFlow: Learning Optical Flow with Unlabeled Data Distillation (2019) arXiv
  15. Lindeberg, Tony: Spatio-temporal scale selection in video data (2018)
  16. Xinyu Huang, Peng Wang, Xinjing Cheng, Dingfu Zhou, Qichuan Geng, Ruigang Yang: The ApolloScape Open Dataset for Autonomous Driving and its Application (2018) arXiv
  17. 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)
  18. Burger, Martin; Dirks, Hendrik; Frerking, Lena: On optical flow models for variational motion estimation (2017)
  19. Coninx, Alexandre; Bessière, Pierre; Droulez, Jacques: Quick and energy-efficient Bayesian computing of binocular disparity using stochastic digital signals (2017)
  20. Hollósi, Gergely; Lukovszki, Csaba; Moldován, István; Plósz, Sándor; Harasztos, Frigyes: Monocular indoor localization techniques for smartphones (2016)

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