论文标题
了解LiDAR对象检测网络中的域间隙
Understanding the Domain Gap in LiDAR Object Detection Networks
论文作者
论文摘要
为了使自主行动成为现实，人工神经网络必须在开放世界中可靠地工作。但是，开放世界是巨大且不断变化的，因此在技术上不可行，可以准确地代表该领域的培训数据集。因此，必须了解训练数据集和开放世界之间的域差距。在这项工作中，我们研究了对象检测网络中高分辨率和低分辨率激光雷达传感器之间的域间隙。使用独特的数据集，使我们能够研究传感器分辨率域间隙独立于其他效果，我们显示了两个不同的域间隙 - 推理域间隙和训练域间隙。推理域间隙的特征是对每个对象的激光点数的数量有很强的依赖性，而训练差距没有这种依赖性。这些fndings表明，需要采用不同的方法来弥合这些推理和训练域差距。
In order to make autonomous driving a reality, artificial neural networks have to work reliably in the open-world. However, the open-world is vast and continuously changing, so it is not technically feasible to collect and annotate training datasets which accurately represent this domain. Therefore, there are always domain gaps between training datasets and the open-world which must be understood. In this work, we investigate the domain gaps between high-resolution and low-resolution LiDAR sensors in object detection networks. Using a unique dataset, which enables us to study sensor resolution domain gaps independent of other effects, we show two distinct domain gaps - an inference domain gap and a training domain gap. The inference domain gap is characterised by a strong dependence on the number of LiDAR points per object, while the training gap shows no such dependence. These fndings show that different approaches are required to close these inference and training domain gaps.
