nuScenes is a public large-scale dataset for autonomous driving, providing a comprehensive suite of sensor data and annotations.
nuScenes is a public, large-scale dataset designed to advance research in autonomous driving. It provides a comprehensive collection of sensor data, including images from six cameras, LiDAR point clouds, radar data, GPS information, and vehicle telemetry, all synchronized and annotated. The dataset covers diverse driving scenarios in urban environments, collected in Boston and Singapore. nuScenes is primarily used for tasks such as object detection, tracking, and scene understanding. Researchers and developers in the fields of robotics, computer vision, and autonomous driving leverage nuScenes to train and evaluate their algorithms. It includes detailed 3D bounding box annotations for a wide range of objects, semantic map information, and attributes for each object instance.
nuScenes is a public, large-scale dataset designed to advance research in autonomous driving.
Explore all tools that specialize in object detection in 3d space. This domain focus ensures nuScenes delivers optimized results for this specific requirement.
Explore all tools that specialize in object tracking across multiple frames. This domain focus ensures nuScenes delivers optimized results for this specific requirement.
Explore all tools that specialize in scene understanding and semantic segmentation. This domain focus ensures nuScenes delivers optimized results for this specific requirement.
Explore all tools that specialize in sensor fusion using camera, lidar, and radar data. This domain focus ensures nuScenes delivers optimized results for this specific requirement.
Explore all tools that specialize in motion forecasting for surrounding vehicles and pedestrians. This domain focus ensures nuScenes delivers optimized results for this specific requirement.
Explore all tools that specialize in map creation and localization. This domain focus ensures nuScenes delivers optimized results for this specific requirement.
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