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One Million Scenes for Autonomous Driving (ONCE)
The ONCE dataset comprises 1 million LiDAR scenes and 7 million corresponding camera images. -
Woodscape dataset
Woodscape is a multi-task, multi-camera fisheye dataset for autonomous driving. -
SemanticKITTI: A dataset for semantic scene understanding of lidar sequences
SemanticKITTI: A dataset for semantic scene understanding of lidar sequences. -
TORNADO-Net: Multiview Total Variation Semantic Segmentation with Diamond Inc...
Semantic segmentation of point clouds is a key component of scene understanding for robotics and autonomous driving. In this paper, we introduce TORNADO-Net - a neural network... -
LAV Dataset
The LAV dataset is used to evaluate the robustness of the proposed Penalty-based Imitation Learning with Cross Semantics Generation approach. -
DeepAccident
A motion and accident prediction benchmark for v2x autonomous driving -
Waymo Open Dataset and nuScenes Dataset
The Waymo Open Dataset and the nuScenes Dataset are used to evaluate the performance of the AFDetV2 model. -
nuScenes: A multimodal dataset for autonomous driving
nuScenes: A multimodal dataset for autonomous driving. -
Argoverse 2 challenge on 4D occupancy forecasting at the workshop on autonomo...
Argoverse 2 challenge on 4D occupancy forecasting at the workshop on autonomous driving -
ROAD and SARAS-ESAD datasets for complex activity detection
A dataset for complex activity detection in autonomous driving and surgical robotics. -
KITTI 2015 dataset
KITTI 2015 dataset contains videos in 200 street scenes captured by RGB cameras, with sparse depth ground truths captured by Velodyne laser scanner. -
KITTI: A Benchmark for 3D Object Detection in Autonomous Driving
A widely-adopted multi-modality benchmark for 3D object detection. -
nuscenes: A Multi-Modal Dataset for Autonomous Driving
A large-scale benchmark for autonomous driving with 1,000 scenes. -
ROAD-R: the autonomous driving dataset with logical requirements
The ROAD-R dataset contains autonomous driving scenarios with logical requirements. -
DSEC: A Stereo Event Camera Dataset for Driving Scenarios
A new dataset that contains demanding illumination conditions and provides a rich set of sensory data for autonomous driving. -
JPerceiver: Joint Perception Network for Depth, Pose and Layout Estimation in...
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... -
NeRFs for Autonomous Driving
Neural Radiance Fields (NeRFs) have emerged as promising tools for advancing autonomous driving (AD) research, offering scalable closed-loop simulation and data augmentation...