67 datasets found

Reconstructing large-scale 3D scenes is essential for autonomous vehicles, especially when partial sensor data is lost.

Reconstructing large-scale 3D scenes is essential for autonomous vehicles, especially when partial sensor data is lost.

The dataset used in the paper is a real-world driving dataset (Drive4D), training on over 1 million frames from 5,000 scenarios and validating on 5,000 frames from 500...

The KITTI Visual Odometry benchmark dataset consists of 22 stereo sequences with ground truth trajectories.

LidarDM: A novel layout-conditioned latent diffusion model for generating realistic LiDAR point clouds.

3D object detection is one of the most important tasks in autonomous driving and robotics. Our research focuses on tackling low efficiency issue of point-based methods on...

The One-Class Road Detection Dataset is a dataset for road detection, which is a challenging issue in autonomous driving.

The KITTI 3D Object Detection dataset is used for training and testing the proposed Shift R-CNN model.

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...

Scalability in perception for autonomous driving: Waymo open dataset.

KITTI-3D benchmark contains a train-ing set of 7481 images and a test set of 7518 images.

The Argoverse 2 motion forecasting dataset contains 250,000 driving scenarios, each 11 seconds long. These scenarios cover 6 geographical regions and represent 763 total hours...

The KITTI odometry dataset is a collection of 22 sequences, containing point clouds, images, and GPS recordings of inner-city traffic, residential areas, highway scenes, and...

The Argoverse dataset includes 65 training and 24 validation sequences recorded in Miami and Pittsburgh.

The CARLA simulator dataset is used for vehicle trajectory control. It contains a test track at ZF Friedrichshafen AG, where the start position is indicated by an arrow and the...

The ONCE dataset comprises 1 million LiDAR scenes and 7 million corresponding camera images.

nuScenes: A multimodal dataset for autonomous driving.

KITTI 2015 dataset contains videos in 200 street scenes captured by RGB cameras, with sparse depth ground truths captured by Velodyne laser scanner.

A widely-adopted multi-modality benchmark for 3D object detection.

A large-scale benchmark for autonomous driving with 1,000 scenes.