67 datasets found

NVRadarNet: Real-Time Radar Obstacle and Free Space Detection for Autonomous Driving

The VPR-NuScenes dataset is a large-scale dataset for visual place recognition, consisting of 850 annotated scenes with 20s-long sequences of consecutive frames.

The nuScenes dataset is a large-scale dataset for autonomous driving, containing 100,000 scenes with 10 Hz sampling frequency.

The Apolloscape dataset is a large-scale dataset for autonomous driving, containing 75 scenarios with 10 Hz sampling frequency.

LiDAR simulation aims at generating realistic LiDAR data with labels for training and verifying self-driving algorithms more efficiently.

The Waymo Open dataset is a large-scale video dataset used for testing object-centric video models.

Panoptic nuScenes is a multimodal dataset for lidar panoptic segmentation and tracking.

LiDAR segmentation is crucial for autonomous driving systems. The recent range-view approaches are promising for real-time processing. However, they suffer inevitably from...

The dataset used for training and evaluation of the proposed framework for unsupervised metric relocalization.

The Temporal Occupancy Flow Graph (TOFG) is a unified environment representation that unifies the map information and vehicle trajectories in a homogeneous graph.

A LiDAR simulator for autonomous driving that generates annotated point cloud data.

Scalability in perception for autonomous driving: Waymo open dataset.

The ScribbleKITTI dataset is a dataset for weakly-supervised LiDAR semantic segmentation. It consists of a subset of the SemanticKITTI dataset with weak labels.

The KITTI dataset is a large-scale dataset for autonomous driving, consisting of 15,000 images with annotated objects.

The KITTI 3D dataset consists of 7,481 images for training and 7,518 images for testing. The labels of the train set are publicly available and the labels of the test set are...

The Automotive Radar Dataset is a dataset containing data collected from a scanning Navtech radar in various weather conditions.

RADIATE is a radar dataset for automotive perception in bad weather, including 3 hours of annotated radar images with over 200K labelled road actors.

The dataset used for training and testing the proposed EDNet model for efficient disparity estimation.

The KITTI benchmark dataset is used to evaluate the performance of the proposed method. The dataset contains large-scale outdoor sequences of images captured by a forward-facing...

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