We propose the Temporal Point Cloud Networks (TPCN), a novel and flexible framework with joint spatial and temporal learning for trajectory prediction. Unlike existing approaches that rasterize agents and map information as 2D images or operate in a graph representation, our approach extends ideas from point cloud learning with dynamic temporal learning to capture both spatial and temporal information by splitting trajectory prediction into both spatial and temporal dimensions. In the spatial dimension, agents can be viewed as an unordered point set, and thus it is straightforward to apply point cloud learning techniques to model agents' locations. While the spatial dimension does not take kinematic and motion information into account, we further propose dynamic temporal learning to model agents' motion over time. Experiments on the Argoverse motion forecasting benchmark show that our approach achieves the state-of-the-art results.

中文翻译：

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TPCN：用于运动预测的时间点云网络

我们提出了时间点云网络（TPCN），这是一种新颖的，灵活的，具有联合时空学习的轨迹预测框架。与将代理商和地图信息栅格化为2D图像或以图形表示形式运行的现有方法不同，我们的方法将点云学习与动态时间学习的思想扩展到了通过将轨迹预测分为空间和时间维度来捕获空间和时间信息的思想。在空间维度上，代理可以看作是无序的点集，因此可以直接将点云学习技术应用于代理位置的建模。虽然空间维度未考虑运动学和运动信息，但我们进一步提出了动态时间学习来模拟随时间变化的智能体运动。