26 datasets found

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

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 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.

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.

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...

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

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...

Lidar panoptic segmentation for autonomous driving

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

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

BEVFusion: Multi-task multi-sensor fusion with unified bird’s-eye view representation

The ONCE dataset is a large-scale dataset for autonomous driving, containing 581 sequences composed of 20 labeled frames and 561 unlabeled frames.

The KITTI Object Detection Benchmark consists of 7,481 training images and 7,518 testing images, with 3D LiDAR point clouds and camera images.

A benchmark for stereo matching and depth estimation.

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