数据集Nuscence

数据集

数据集地址：Nuscence

论文

论文地址：nuScenes: A multimodal dataset for autonomous driving

论文解读

摘要

Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology. Image based benchmark datasets have driven development in computer vision tasks such as object detection, tracking and segmentation of agents in the environment. Most autonomous vehicles, however, carry a combination of cameras and range sensors such as lidar and radar. As machine learning based methods for detection and tracking become more prevalent, there is a need to train and evaluate such methods on datasets containing range sensor data along with images.
稳健的物体检测和跟踪对于自动驾驶车辆技术的部署至关重要。基于图像的基准数据集推动了计算机视觉任务的发展，如环境中物体的检测、跟踪和分割。然而，大多数自动驾驶车辆配备了摄像头和距离传感器的组合，例如激光雷达和雷达。随着基于机器学习的检测和跟踪方法变得越来越普遍，迫切需要在包含距离传感器数据和图像的多模态数据集上训练和评估这些方法。

In this work we present nuTonomy scenes (nuScenes), the first dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view. nuScenes comprises 1000 scenes, each 20s long and fully annotated with 3D bounding boxes for 23 classes and 8 attributes. It has 7x as many annotations and 100x as many images as the pioneering KITTI dataset. We define novel 3D detection and tracking metrics. We also provide careful dataset analysis as well as baselines for lidar and image based detection and tracking. Data, development kit and more information are available online.
在这项工作中，我们提出了nuTonomy scenes (nuScenes)，这是第一个包含全套自动驾驶车辆传感器的数据集：6个摄像头、5个雷达和1个激光雷达，全部具有360度视野。nuScenes包含1000个场景，每个场景时长20秒，全部用3D边界框为23类和8种属性进行了完整标注。它的标注数量是开创性的KITTI数据集的7倍，图像数量是KITTI的100倍。我们定义了新的3D检测和跟踪评估指标。我们还提供了对数据集的仔细分析，并为基于激光雷达和图像的检测和跟踪提供了基线模型。数据、开发工具包及更多信息均可在线获取。