The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Vision meets robotics: The KITTI dataset
Abstract
We present a novel dataset captured from a VW station wagon for use in mobile robotics and autonomous driving research. In total, we recorded 6 hours of traffic scenarios at 10–100 Hz using a variety of sensor modalities such as high-resolution color and grayscale stereo cameras, a Velodyne 3D laser scanner and a high-precision GPS/IMU inertial navigation system. The scenarios are diverse, capturing real-world traffic situations, and range from freeways over rural areas to inner-city scenes with many static and dynamic objects. Our data is calibrated, synchronized and timestamped, and we provide the rectified and raw image sequences. Our dataset also contains object labels in the form of 3D tracklets, and we provide online benchmarks for stereo, optical flow, object detection and other tasks. This paper describes our recording platform, the data format and the utilities that we provide.
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In the citing paper
“Fisheye cameras have a strong radial distortion that cannot be corrected without disadvantages, including reduced field-of-view and resampling distortion artifacts at the periphery [1].”
From OmniDet: Surround View Cameras Based Multi-Task Visual Perception Network for Autonomous Driving (2021) · cites this paper as [1]
In the citing paper
“The proposed approach can, in principle, be applied to any fisheye projection model of choice explained in [1].”
From OmniDet: Surround View Cameras Based Multi-Task Visual Perception Network for Autonomous Driving (2021) · cites this paper as [1]
In the citing paper
“| Segmentation | ENet [41] | IoU | 51.4 |”
From WoodScape: A Multi-Task, Multi-Camera Fisheye Dataset for Autonomous Driving (2019) · cites this paper as [41]
In the citing paper
“While widely used driving datasets such as KITTI [1], nuScenes [2], and Waymo [3] support perception tasks under natural conditions, they do not provide systematic control over degradation types and severity.”
From DRIVE-C: A Controlled Corruption Dataset for Autonomous Driving (2026) · cites this paper as [1]
In the citing paper
“While camera systems are crucial for applications involving driverless vehicles, unfavorable weather conditions such as fog, intense rain, snow, and dust reduce their effectiveness.Although sophisticated techniques for image processing try to solve these problems, accuracy and dependability issues still exist, which presents significant obstacles to the advancement of autonomous driving [51].”
From Object Detection in Autonomous Vehicles under Adverse Weather: A Review of Traditional and Deep Learning Approaches (2024) · cites this paper as [51]
In the citing paper
“KITTI [121] Real”
From Vehicle-to-Everything Communication in Intelligent Connected Vehicles: A Survey and Taxonomy (2025) · cites this paper as [121]
In the citing paper
“Most of the reviewed datasets provide ground-truth labels for 2D object detection and semantic segmentation tasks [60], [75], [88], [90]-[93].”
From Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges (2021) · cites this paper as [75]
In the citing paper
“Even though the KITTI dataset [75] is widely used for autonomous driving research, the diversity of its recording conditions is relatively low: it is recorded in Karlsruhe -a mid-sized city in Germany, only during daytime and on sunny days.”
From Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges (2021) · cites this paper as [75]
In the citing paper
“Car, Van, Truck, Pedestrian, Person (sitting), Cyclist, Tram, Misc”
From Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges (2021) · cites this paper as [75]
In the citing paper
“7481 frames (training) 80 . 256 objects”
From Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges (2021) · cites this paper as [75]
In the citing paper
“Karlsruhe”
From Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges (2021) · cites this paper as [75]
In the citing paper
“2D/3D bounding box, visual odometry, road, optical flow, tracking, depth, 2D instance and pixel-level segmentation”
From Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges (2021) · cites this paper as [75]
In the citing paper
“2012, 2013, 2015”
From Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges (2021) · cites this paper as [75]
In the citing paper
“Visual (Stereo) camera, 3D LiDAR, GNSS, and inertial sensors”
From Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges (2021) · cites this paper as [75]
In the citing paper
“Monocular VO approaches recover translation up to an unknown scale [13], [14], [15], [16] as depth is unobservable from monocular cameras and thus there is ambiguity in the scale of translational estimates.”
From Unsupervised Deep Visual-Inertial Odometry with Online Error Correction for RGB-D Imagery (2020) · cites this paper as [15]
In the citing paper
“It is known as the most prestigious dataset in the self-driving vehicles domain. To this date it counts more than 2000 citations in the literature.”
From A survey of deep learning techniques for autonomous driving (2020) · cites this paper as [151]
In the citing paper
“Summary KITTI [8,65-67] was the first large-scale dataset to tackle the important issue of multimodal data in autonomous vehicles.”
From Multimodal semantic segmentation in autonomous driving: A review of current approaches and future perspectives (2022) · cites this paper as [65]
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Self-citationEfficient representation of traffic scenes by means of dynamic stixels
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FAB-MAP 3D: Topological mapping with spatial and visual appearance
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- Geiger A, 2011, Conference on neural information processing systems (NIPS)
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Metadata
- Authors
- Geiger, A., Lenz, P., Stiller, C., & Urtasun, R.
- Affiliations
- Karlsruhe Institute of Technology (DE)
- Toyota Technological Institute at Chicago (US)
- Venue
- The International Journal of Robotics Research
- Publisher
- SAGE Publications
- Published
- 2013-08-23
- Volume
- 32
- Issue
- 11
- Pages
- 1231-1237
- ISSN
- 0278-3649 (ISSN-L)
- 1741-3176
- OpenAlex
- W2115579991
- Cited by
- 9,913
- References
- 13
- Citations by year
- 2026: 810 · 2025: 1281 · 2024: 1350 · 2023: 1268 · 2022: 1116 · 2021: 1148 · 2020: 951 · 2019: 808 · 2018: 471 · 2017: 283 · 2016: 184 · 2015: 163 · 2014: 72 · 2013: 5
- FWCI
- 219.28
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The International Journal of Robotics Research (publishedVersion)
- Max Planck Digital Library (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotics and Sensor-Based Localization
- Keywords
- KITTI dataset, autonomous driving, mobile robotics, traffic scenarios, stereo vision, 3D laser scanning, sensor synchronization, object detection, optical flow, benchmarking
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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