75 datasets found

The KITTI odometry benchmark dataset is used for evaluating computer vision algorithms in autonomous driving scenarios. It contains LiDAR data collected with Velodyne HDL-64E,...

The NYU DublinCity LiDAR dataset is a high resolution collection of 41 LiDAR scans over an area of Dublin, Ireland.

The dataset used in the paper is a large-scale point cloud compression framework, which can organize sparse and un-structured point clouds in a memory-efficient way.

LiDAR odometry and mapping algorithm for LiDARs with small FoVs

The dataset used in the paper is a driving scenario dataset, which contains camera and LiDAR data.

The dataset is used for lane detection in highway and city scenes.

The dataset used in the paper is a LiDAR point cloud dataset, which is a sparse 3D point cloud acquired by LiDAR sensors.

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

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...

A dataset of vehicular wireless channel measurements, including LiDAR and Radar data.

Real-world problem with LiDAR sensory data has been used in the experiment. The dataset is obtained by sending an inspection gauge with the attached LiDAR sensor through the...

This work proposes a method for depth completion of sparse LiDAR data using a convolutional neural network which can be used to generate semi-dense depth maps and 3D...

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

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

A method for LiDAR 3D point-cloud semantic segmentation exploiting perceptual information from RGB images and spatial-depth information from point clouds.

A large-scale dataset for LiDAR-based semantic labeling, created using the Autolabeling process.

A large-scale automated cross-modal training data generation process for boosting the LiDAR-based semantic labeling performance.

The dataset used for training and testing the proposed localizability estimation approach.

A real-world dataset for LiDAR-based end-to-end navigation, consisting of 32km of driving data with 29km for training and 3km for testing.