nuScenes

The nuScenes dataset is a large-scale autonomous driving dataset with 3d object annotations. It features: ¡ñ Full sensor suite (1x LIDAR, 5x RADAR, 6x camera, IMU, GPS) ¡ñ 1000 scenes of 20s each ¡ñ 1,400,000 camera images ¡ñ 390,000 lidar sweeps ¡ñ Two diverse cities: Boston and Singapore ¡ñ Left versus right hand traffic ¡ñ Detailed map information ¡ñ 1.4M 3D bounding boxes manually annotated for 23 object classes ¡ñ Attributes such as visibility, activity and pose ¡ñ New: 1.1B lidar points manually annotated for 32 classes ¡ñ New: Explore nuScenes on SiaSearch ¡ñ Free to use for non-commercial use ¡ñ For a commercial license contact [e-mail] For the nuScenes dataset we collect approximately 15h of driving data in Boston and Singapore. For the full nuScenes dataset, we publish data from Boston Seaport and Singapore¡¯s One North, Queenstown and Holland Village districts. Driving routes are carefully chosen to capture challenging scenarios. We aim for a diverse set of locations, times and weather conditions. To balance the class frequency distribution, we include more scenes with rare classes (such as bicycles). Using these criteria, we manually select 1000 scenes of 20s duration each. These scenes are carefully annotated using human experts. The annotator instructions can be found in the devkit repository. We use two Renault Zoe cars with an identical sensor layout to drive in Boston and Singapore. The data was gathered from a research platform and is not indicative of the setup used in Motional products. Please refer to the above figure for the placement of the sensors. We release data from the following sensors: To achieve a high quality multi-sensor dataset, it is essential to calibrate the extrinsics and intrinsics of every sensor. We express extrinsic coordinates relative to the ego frame, i.e. the midpoint of the rear vehicle axle. The most relevant steps are described below: In order to achieve good cross-modality data alignment between the LIDAR and the cameras, the exposure of a camera is triggered when the top LIDAR sweeps across the center of the camera¡¯s FOV. The timestamp of the image is the exposure trigger time; and the timestamp of the LIDAR scan is the time when the full rotation of the current LIDAR frame is achieved. Given that the camera¡¯s exposure time is nearly instantaneous, this method generally yields good data alignment. Note that the cameras run at 12Hz while the LIDAR runs at 20Hz. The 12 camera exposures are spread as evenly as possible across the 20 LIDAR scans, so not all LIDAR scans have a corresponding camera frame. Reducing the frame rate of the cameras to 12Hz helps to reduce the compute, bandwidth and storage requirement of the perception system. It is our priority to protect the privacy of third parties. For this purpose we use state-of-the-art object detection techniques to detect license plates and faces. We aim for a high recall and remove false positives that do not overlap with the reprojections of the known person and car boxes. Eventually we use the output of the object detectors to blur faces and license plates in the images of nuScenes.