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 )

Showing results 41 to 40 of 40.
Sorted by year (citations)

1 2 next

  1. Chen, Zhe; Zhang, Jing; Tao, Dacheng: Recursive context routing for object detection (2021)
  2. Luo, Wenhan; Xing, Junliang; Milan, Anton; Zhang, Xiaoqin; Liu, Wei; Kim, Tae-Kyun: Multiple object tracking: a literature review (2021)
  3. Marcos Nieto, Orti Senderos, Oihana Otaegui: Boosting AI applications: Labeling format for complex datasets (2021) not zbMATH
  4. 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
  5. Suchan, Jakob; Bhatt, Mehul; Varadarajan, Srikrishna: Commonsense visual sensemaking for autonomous driving -- on generalised neurosymbolic online abduction integrating vision and semantics (2021)
  6. Wu, Zhenni; Chen, Hengxin; Fang, Bin; Li, Zihao; Chen, Xinrun: Building pose estimation from the perspective of UAVs based on CNNs (2021)
  7. Zeng, Chao; Ng, Michael K.: Incremental CP tensor decomposition by alternating minimization method (2021)
  8. Zöller, Marc-André; Huber, Marco F.: Benchmark and survey of automated machine learning frameworks (2021)
  9. Bredies, Kristian; Holler, Martin: Higher-order total variation approaches and generalisations (2020)
  10. Chin, Tat-Jun; Cai, Zhipeng; Neumann, Frank: Robust fitting in computer vision: easy or hard? (2020)
  11. Qin, Zixuan; Yin, Mengxiao; Li, Guiqing; Yang, Feng: SP-Flow: self-supervised optical flow correspondence point prediction for real-time SLAM (2020)
  12. Ranjan, Anurag; Hoffmann, David T.; Tzionas, Dimitrios; Tang, Siyu; Romero, Javier; Black, Michael J.: Learning multi-human optical flow (2020)
  13. Sharma, Vipul; Mir, Roohie Naaz: A comprehensive and systematic look up into deep learning based object detection techniques: a review (2020)
  14. Song, Taeyong; Kim, Youngjung; Oh, Changjae; Jang, Hyunsung; Ha, Namkoo; Sohn, Kwanghoon: Simultaneous deep stereo matching and dehazing with feature attention (2020)
  15. Stutz, David; Geiger, Andreas: Learning 3D shape completion under weak supervision (2020)
  16. Datta, Amitava; Kaur, Amardeep; Lauer, Tobias; Chabbouh, Sami: Exploiting multi-core and many-core parallelism for subspace clustering (2019)
  17. 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
  18. 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
  19. Lee, Jeong-Kyun; Yoon, Kuk-Jin: Joint estimation of camera orientation and vanishing points from an image sequence in a non-Manhattan world (2019)
  20. 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)

1 2 next