Are we ready for autonomous driving? The KITTI vision benchmark suite
Abstract
Today, visual recognition systems are still rarely employed in robotics applications. Perhaps one of the main reasons for this is the lack of demanding benchmarks that mimic such scenarios. In this paper, we take advantage of our autonomous driving platform to develop novel challenging benchmarks for the tasks of stereo, optical flow, visual odometry/SLAM and 3D object detection. Our recording platform is equipped with four high resolution video cameras, a Velodyne laser scanner and a state-of-the-art localization system. Our benchmarks comprise 389 stereo and optical flow image pairs, stereo visual odometry sequences of 39.2 km length, and more than 200k 3D object annotations captured in cluttered scenarios (up to 15 cars and 30 pedestrians are visible per image). Results from state-of-the-art algorithms reveal that methods ranking high on established datasets such as Middlebury perform below average when being moved outside the laboratory to the real world. Our goal is to reduce this bias by providing challenging benchmarks with novel difficulties to the computer vision community. Our benchmarks are available online at: www.cvlibs.net/datasets/kitti.
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Weng and Kitani presented a similar 3D MOT benchmark on KITTI.
“Weng and Kitani [77] presented a similar 3D MOT benchmark on KITTI [32].”
From nuScenes: A Multimodal Dataset for Autonomous Driving (2020) · cites this paper as [32]
State-of-the-art algorithms are currently being evaluated on the KITTI dataset and nuScenes dataset as they offer labeled 3D scenes.
“State-of-the-art algorithms are currently being evaluated on the KITTI dataset [177] and nuScenes dataset [178] as they offer labeled 3D scenes.”
From A Survey of Autonomous Driving: <i>Common Practices and Emerging Technologies</i> (2020) · cites this paper as [178]
The nuScenes dataset is the most recent urban driving dataset with lidar and image sensors.
“The nuScenes dataset is the most recent urban driving dataset with lidar and image sensors [178].”
From A Survey of Autonomous Driving: <i>Common Practices and Emerging Technologies</i> (2020) · cites this paper as [178]
This has driven a shift from explicit to implicit fusion, where network design inherently captures modality relationships, enhancing deep semantic extraction.
“This has driven a shift from explicit to implicit fusion [37], where network design inherently captures modality relationships, enhancing deep semantic extraction.”
From Multimodal fusion and vision–language models: A survey for robot vision (2026) · cites this paper as [37]
Multimodal fusion combines sources such as vision, language, depth, LiDAR, and speech to enhance scene semantic understanding.
“It combines various sources of information, such as vision, language, depth data, lidar, and speech, to enhance the capability of scene semantic understanding [35,36,37].”
From Multimodal fusion and vision–language models: A survey for robot vision (2026) · cites this paper as [37]
The KITTI odometry dataset is a benchmark for the development and evaluation of VO algorithms.
“We conduct our experiments with the KITTI odometry dataset [16], which is a benchmark for the development and evaluation of VO algorithms.”
From Transformer-Based Model for Monocular Visual Odometry: A Video Understanding Approach (2023) · cites this paper as [16]
The use of public databases such as PASCAL, MS-COCO, or autonomous driving simplifies the performance of experiments to compare sampling methods.
“The use of public databases such as PASCAL [72], MS-COCO [73], or autonomous driving [74-76] simplifies the performance of experiments to compare sampling methods.”
From Ten Years of Active Learning Techniques and Object Detection: A Systematic Review (2023) · cites this paper as [74]
KITTI provides 200k 3D boxes over 22 scenes which helped advance the state-of-the-art in 3D object detection.
“It provides 200k 3D boxes over 22 scenes which helped advance the state-of-the-art in 3D object detection.”
From nuScenes: A Multimodal Dataset for Autonomous Driving (2020) · cites this paper as [32]
At the time of the initial nuScenes release, only a few datasets annotated objects using 3D boxes, and they did not provide the full sensor suite.
“At the time of the initial nuScenes release, only a few datasets annotated objects using 3D boxes [32, 41, 61], and they did not provide the full sensor suite.”
From nuScenes: A Multimodal Dataset for Autonomous Driving (2020) · cites this paper as [32]
KITTI was the pioneering multimodal dataset providing dense pointclouds from a lidar sensor as well as front-facing stereo images and GPS/IMU data.
“KITTI [32] was the pioneering multimodal dataset providing dense pointclouds from a lidar sensor as well as front-facing stereo images and GPS/IMU data.”
From nuScenes: A Multimodal Dataset for Autonomous Driving (2020) · cites this paper as [32]
The paper uses the Average Precision (AP) metric, but defines a match by thresholding the 2D center distance d on the ground plane instead of intersection over union (IOU).
“We use the Average Precision (AP) metric [32, 26], but define a match by thresholding the 2D center distance d on the ground plane instead of intersection over union (IOU).”
From nuScenes: A Multimodal Dataset for Autonomous Driving (2020) · cites this paper as [32]
Such localization systems are vulnerable to GPS outages, as seen on the KITTI dataset.
“Such localization systems are vulnerable to GPS outages, as seen on the KITTI dataset [32, 7].”
From nuScenes: A Multimodal Dataset for Autonomous Driving (2020) · cites this paper as [32]
Most existing datasets provide the vehicle location based on GPS and IMU.
“Most existing datasets provide the vehicle location based on GPS and IMU [32, 41, 19, 61].”
From nuScenes: A Multimodal Dataset for Autonomous Driving (2020) · cites this paper as [32]
mAP with a threshold on IOU is perhaps the most popular metric for object detection.
“mAP with a threshold on IOU is perhaps the most popular metric for object detection [32, 19, 21].”
From nuScenes: A Multimodal Dataset for Autonomous Driving (2020) · cites this paper as [32]
DPT can be fine-tuned to small monocular depth prediction datasets, such as NYUv2 and KITTI, where it also sets the new state of the art.
“The architecture can also be fine-tuned to small monocular depth prediction datasets, such as NYUv2 [35] and KITTI [15], where it also sets the new state of the art.”
From Vision Transformers for Dense Prediction (2021) · cites this paper as [15]
The biggest gains in performance for zero-shot transfer were achieved for datasets that feature dense, high-resolution evaluations.
“We notice that the biggest gains in performance for zero-shot transfer were achieved for datasets that feature dense, high-resolution evaluations [15,55,59].”
From Vision Transformers for Dense Prediction (2021) · cites this paper as [15]
The KITTI dataset was collected in Karlsruhe.
“Karlsruhe”
From Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges (2021) · cites this paper as [6]
The KITTI dataset uses visual (stereo) camera, 3D LiDAR, GNSS, and inertial sensors.
“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 [6]
The KITTI dataset includes Car, Van, Truck, Pedestrian, Person (sitting), Cyclist, Tram, Misc.
“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 [6]
The KITTI dataset is for 2D/3D bounding box, visual odometry, road, optical flow, tracking, depth, 2D instance and pixel-level segmentation.
“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 [6]
The KITTI dataset contains 7481 training frames and 80,256 objects.
“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 [6]
The KITTI dataset was collected in 2012, 2013, 2015.
“2012, 2013, 2015”
From Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges (2021) · cites this paper as [6]
KITTI was the first large-scale dataset to tackle the important issue of multimodal data in autonomous vehicles.
“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 [8]
KITTI, Caltech and TDC datasets have been collected in one city only; CityPersons was recorded in 27 different cities but, apart from Strasbourg and Zurich, covers only Germany and recordings were not made throughout all seasons.
“KITTI, Caltech and TDC datasets have been collected in one city only. CityPersons was recorded in 27 different cities but, apart of Strasbourg and Zurich, it covers only Germany and recordings were not made throughout all seasons.”
From EuroCity Persons: A Novel Benchmark for Person Detection in Traffic Scenes (2019) · cites this paper as [3]
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“cs applications is challenging because the contextual information can come from multiple sensors with different data modalities. Examples include autonomous vehicles that reason from multiple sensors [2] or robots that jointly reason over perception and language instructions [26]. In such applications the way informat…”
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- Authors
- Geiger, A., Lenz, P., & Urtasun, R.
- Affiliations
- Karlsruhe Institute of Technology (DE)
- Venue
- 2012 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2012-06
- Pages
- 3354-3361
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2150066425
- Semantic Scholar
- de5b0fd02ea4f4d67fe3ae0d74603b9822df4e42
- Cited by
- 14,897
- References
- 49
- Citations by year
- 2026: 1055 · 2025: 1765 · 2024: 2047 · 2023: 1921 · 2022: 1669 · 2021: 1726 · 2020: 1320 · 2019: 1174 · 2018: 765 · 2017: 524 · 2016: 345 · 2015: 269 · 2014: 157 · 2013: 133 · 2012: 23
- FWCI
- 132.22
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Engineering › Aerospace Engineering › Robotics and Sensor-Based Localization
- Keywords
- autonomous driving, benchmark dataset, stereo vision, optical flow, visual odometry, SLAM, 3D object detection, cluttered scenes, real-world evaluation, KITTI dataset
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