论文标题
点数：具有多山光的准确3D边界框估计
Pointillism: Accurate 3D bounding box estimation with multi-radars
论文作者
论文摘要
自主感知需要以3D边界框的动态对象的形式传感高质量的环境。汽车系统中使用的主要传感器是光基相机和激光镜头。但是，众所周知，它们在不利天气条件下失败。雷达可能会解决此问题，因为它们几乎不受不利天气条件的影响。但是，无线信号的镜面反射会导致雷达点云的性能不佳。我们介绍了Pointillism，该系统将来自多个空间分离雷达的数据与最佳分离结合在一起，以减轻这些问题。我们引入了一个新颖的跨电位云概念，该概念使用了由多个雷达引起的空间多样性，并解决了雷达点云中噪声和稀疏性问题。此外，我们介绍了RP-NET的设计，RP-NET是一种新颖的深度学习体系结构，明确设计用于雷达的稀疏数据分布，以实现准确的3D边界盒估计。本文设计和提出的空间技术是雷达点云分布的基础，并将受益于其他雷达传感应用。
Autonomous perception requires high-quality environment sensing in the form of 3D bounding boxes of dynamic objects. The primary sensors used in automotive systems are light-based cameras and LiDARs. However, they are known to fail in adverse weather conditions. Radars can potentially solve this problem as they are barely affected by adverse weather conditions. However, specular reflections of wireless signals cause poor performance of radar point clouds. We introduce Pointillism, a system that combines data from multiple spatially separated radars with an optimal separation to mitigate these problems. We introduce a novel concept of Cross Potential Point Clouds, which uses the spatial diversity induced by multiple radars and solves the problem of noise and sparsity in radar point clouds. Furthermore, we present the design of RP-net, a novel deep learning architecture, designed explicitly for radar's sparse data distribution, to enable accurate 3D bounding box estimation. The spatial techniques designed and proposed in this paper are fundamental to radars point cloud distribution and would benefit other radar sensing applications.
