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.
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Occ3D is a dataset for 3D occupancy prediction, which aims to estimate the detailed occupancy and semantics of objects from multi-view images. To facilitate this task, a label generation pipeline that produces dense, visibility-aware labels for a given scene. This pipeline includes point cloud aggregation, point labeling, and occlusion handling.
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