HD semantic map learning

The nuScenes dataset is a large-scale autonomous driving dataset. The dataset has 3D bounding boxes for 1000 scenes collected in Boston and Singapore. Each scene is 20 seconds long and annotated at 2Hz. This results in a total of 28130 samples for training, 6019 samples for validation and 6008 samples for testing. The dataset has the full autonomous vehicle data suite: 32-beam LiDAR, 6 cameras and radars with complete 360° coverage. The 3D object detection challenge evaluates the performance on 10 classes: cars, trucks, buses, trailers, construction vehicles, pedestrians, motorcycles, bicycles, traffic cones and barriers. Source: PointPainting: Sequential Fusion for 3D Object Detection

The TbV dataset is large-scale dataset created to allow the community to improve the state of the art in machine learning tasks related to mapping, that are vital for self-driving. - Over 1000 scenarios ("logs") captured by a fleet of autonomous vehicles. - 200 logs include real-world lane geometry or crosswalk changes, where an HD map has become stale. - Each log represents a continuous observation of a scene around a self-driving vehicle. - On average, each scenario is 54 seconds in duration. Each scenario has an HD map representing lane boundaries, crosswalks, drivable area, and a raster map of ground height at 0.3 meter resolution. - Captured across 4 seasons in six diverse cities (Austin, TX, Detroit, MI, Miami, FL, Palo Alto, CA, Pittsburgh, PA, and Washington, D.C.) - Includes 559.4K LiDAR Sweeps. - Includes 7.8M Images. - 15.5 hours of driving data. - 180 miles of driving (by the ego-vehicle).