SeeingThroughFog
透过雾
我们引入了具有挑战性的恶劣天气条件下的物体检测基准，涵盖了现实驾驶场景中的12000个样本和雾室内受控天气条件下的1500个样本。 该数据集涵盖了雾，雪和雨等不同的天气状况，并通过北欧10,000多公里的行驶获取。 沿途城市的行车路线如下所示。 总共有10万个Objekt，其中标有准确的2D和3D边界框。 该数据集的主要贡献是：
我们为涵盖信号增强，域自适应，对象检测或多模式传感器融合的各种算法提供了一个试验场，重点是学习传感器之间的鲁棒冗余，特别是如果它们在不同天气条件下不对称失效时。
创建数据集的最初目的是展示在传感器之间学习鲁棒冗余的方法，从而在由于不利天气影响而导致传感器不对称故障的情况下启用原始数据传感器融合。 在我们的案例中，我们脱离了提案级融合，并应用了由测量熵驱动的自适应融合，即使在未知不利天气影响的情况下，也可以进行检测。 该方法的性能优于其他参考融合方法，甚
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