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.
1,494 PAPERS • 20 BENCHMARKS
SemanticKITTI is a large-scale outdoor-scene dataset for point cloud semantic segmentation. It is derived from the KITTI Vision Odometry Benchmark which it extends with dense point-wise annotations for the complete 360 field-of-view of the employed automotive LiDAR. The dataset consists of 22 sequences. Overall, the dataset provides 23201 point clouds for training and 20351 for testing.
517 PAPERS • 10 BENCHMARKS
The Paris-Lille-3D is a Benchmark on Point Cloud Classification. The Point Cloud has been labeled entirely by hand with 50 different classes. The dataset consists of around 2km of Mobile Laser System point cloud acquired in two cities in France (Paris and Lille).
14 PAPERS • 1 BENCHMARK
ScribbleKITTI is a scribble-annotated dataset for LiDAR semantic segmentation.
13 PAPERS • 2 BENCHMARKS
This is a dataset with curb annotations by using 3D LiDAR data and we build this dataset based on the SemanticKITTI dataset.
1 PAPER • NO BENCHMARKS YET
UAV Laser Scanning data collected over neotropical forest (Paracou French Guiana). Four flights conducted over one ha plot in 2021 and 2022.
1 PAPER • 1 BENCHMARK
A cross-city UDA benchmark built upon nuScenes.