SemanticKITTI

by Jens BehleyCC-BY-NC-SA

SemanticKITTI

SemanticKITTI is based on the KITTI Vision Benchmark and we provide semantic annotation for all sequences of the Odometry Benchmark. Overall, we provide an unprecedented number of scans covering the full 360 degree field-of-view of the employed automotive LiDAR. We labeled each scan resulting in a sequence of labeled point clouds, which were recorded at a rate of 10 Hz. This enables the usage of temporal information for semantic scene understanding and aggregation of information over multiple scans. We annotated moving and non-moving traffic participants with distinct classes, including cars, trucks, motorcycles, pedestrians, and bicyclists. This enables to reason about dynamic objects in the scene.

Dataset Attributes

Label SVG
TasksSegmentation
Label SVG
CategoriesAutonomous Driving, Road, Street, Pedestrians, Vehicles
Label SVG
SensorLiDAR

Class Labels

roadsidewalkparkingother-groundbuildingother-structurecartruckbicyclemotorcycleother-vehiclevegetationtrunkterrainpersonbicyclistmotorcyclistfencepoletraffic signother-objectoutlier