KITTI Dataset provides a suite of real-world computer vision benchmarks for autonomous driving research and development.
The KITTI Vision Benchmark Suite is a collection of datasets designed for autonomous driving research. Captured using an autonomous driving platform, it offers benchmarks for tasks like stereo vision, optical flow, visual odometry, and 3D object detection and tracking. The dataset includes images from high-resolution color and grayscale cameras, along with accurate ground truth data from a Velodyne laser scanner and GPS localization. The data is captured in diverse environments, including urban, rural, and highway settings. Up to 15 cars and 30 pedestrians are visible per image. It reduces bias in existing benchmarks by providing real-world scenarios with novel difficulties.
The KITTI Vision Benchmark Suite is a collection of datasets designed for autonomous driving research.
Explore all tools that specialize in detect cars and pedestrians. This domain focus ensures KITTI Dataset delivers optimized results for this specific requirement.
Explore all tools that specialize in estimate ego-motion. This domain focus ensures KITTI Dataset delivers optimized results for this specific requirement.
Explore all tools that specialize in estimate depth from stereo images. This domain focus ensures KITTI Dataset delivers optimized results for this specific requirement.
Provides rectified stereo image pairs with corresponding disparity maps for evaluating stereo matching algorithms. Includes error evaluation functions for training model parameters.
Includes image sequences with ground truth optical flow fields for evaluating motion estimation algorithms. Features both dense and sparse flow annotations.
Offers LiDAR point clouds and synchronized images with 3D bounding box annotations for cars, pedestrians, and cyclists. Evaluation includes 3D and bird's eye view metrics.
Provides sequences of calibrated images and LiDAR data for estimating the ego-motion of the vehicle. Includes ground truth poses for evaluation.
Offers pixel-level semantic labels for images, enabling the training and evaluation of semantic segmentation models for autonomous driving scenes.
Visit the KITTI Dataset website at http://www.cvlibs.net/datasets/kitti/.
Review the available datasets and benchmarks.
Download the desired dataset or development kit.
Read the documentation to understand the data format and evaluation metrics.
Implement your algorithm for the chosen task.
Evaluate your algorithm using the provided evaluation scripts.
Submit your results to the online benchmark server for comparison.
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