nuScenes and VoD

The dataset comprises large-scale, high-resolution multimodal sensor data for 3D object detection, including LiDAR point clouds, synchronized RGB images, and depth maps. It contains ​XX,000​ annotated scenes captured across diverse environments (urban, highway, indoor) under varying lighting and weather conditions. Each scene is densely annotated with 3D bounding boxes for ​XX categories​ (e.g., vehicles, pedestrians, cyclists), with precise localization (x, y, z), orientation (yaw, pitch, roll), and occlusion/truncation labels. The dataset is split into training (XX%), validation (XX%), and test (XX%) sets, with the test set labels withheld to ensure unbiased benchmarking. Challenges include long-tail class distributions, dynamic occlusions, and sparse point cloud regions, reflecting real-world complexity. Auxiliary metadata (calibration parameters, timestamps) and optional scene-level semantic segmentation labels are provided to support multi-task learning.