论文标题
结构化在线地图的层次重复注意网络
Hierarchical Recurrent Attention Networks for Structured Online Maps
论文作者
论文摘要
在本文中，我们解决了从稀疏的3D点云中提取在线道路网络的问题。我们的方法的灵感来自注释者如何构建车道图，首先识别有多少车道，然后依次绘制每个车道。我们开发了一个分层复发网络，该网络可参与车道边界的初始区域，并通过输出结构化的多线线将其完全追踪。我们还提出了一种新型的可区分损失函数，该函数衡量地面真理polyline及其预测边缘的偏差。这比顶点上的距离更合适，因为存在许多方法来绘制等效质量的方法。我们在90公里的高速公路上证明了我们的方法的有效性，并表明我们可以在92 \％的时间内恢复正确的拓扑。
In this paper, we tackle the problem of online road network extraction from sparse 3D point clouds. Our method is inspired by how an annotator builds a lane graph, by first identifying how many lanes there are and then drawing each one in turn. We develop a hierarchical recurrent network that attends to initial regions of a lane boundary and traces them out completely by outputting a structured polyline. We also propose a novel differentiable loss function that measures the deviation of the edges of the ground truth polylines and their predictions. This is more suitable than distances on vertices, as there exists many ways to draw equivalent polylines. We demonstrate the effectiveness of our method on a 90 km stretch of highway, and show that we can recover the right topology 92\% of the time.
