18 datasets found

A real-world stereo dataset containing both indoor and outdoor environments via robot teleoperation.

The dataset used in the paper is not explicitly described, but it is mentioned that the authors used the SHIFT and Berkeley DeepDrive Attention (BDD-A) datasets.

Oxford RobotCar dataset contains a large amount of data collected from one route through central Oxford, and covers various weather and traffic conditions.

Scalability in perception for autonomous driving: Waymo open dataset.

The Oxford Radar RobotCar dataset is a radar extension to the Oxford RobotCar dataset, containing radar observations of a vehicle in various scenarios.

The dataset used in the paper is a collection of images and corresponding affordances (pedestrian hazard, vehicle hazard, red traffic light, and relative heading angle) for...

A new dataset for omnipotent city understanding from satellite and street-level images of multiple views.

The Waymo Open Dataset and the nuScenes Dataset are used to evaluate the performance of the AFDetV2 model.

A new dataset that contains demanding illumination conditions and provides a rich set of sensory data for autonomous driving.

Nuscenes is a large-scale autonomous driving dataset that provides high-resolution BEV semantic occupancy labels for roads and vehicles.

Depth estimation, visual odometry, and bird’s-eye-view scene layout estimation present three critical tasks for driving scene perception, which is fundamental for motion...

The dataset used in the paper is the NuScenes dataset, which contains LiDAR point clouds and corresponding semantic annotations.

KITTI 2012 is a real-world dataset in the outdoor scenario, and contains 194 training and 195 testing stereo image pairs with the size of 376 × 1240.

The Argoverse dataset is a large-scale dataset for autonomous driving, containing 3D point clouds, semantic segmentation masks, and instance segmentation masks.

The CARLA dataset is a complex urban-like environment with multi-agent dynamics, pedestrians, intersections, cross-traffic, roundabout, and changing weather conditions.

The KITTI Vision Benchmark Suite is a dataset used for object detection and tracking in autonomous vehicles.

The dataset used in the paper is the KITTI dataset, which is a benchmark for monocular depth estimation. The dataset consists of a large collection of images and corresponding...

The KITTI 2015 dataset is a real-world dataset of street views, containing 200 training stereo image pairs with sparsely labeled disparity from LiDAR data.