We consider the problem of modeling the dynamics of continuous
spatial-temporal processes represented by irregular samples through both space
and time. Such processes occur in sensor networks, citizen science, multi-robot
systems, and many others. We propose a new deep model that is able to directly
learn and predict over this irregularly sampled data, without voxelization, by
leveraging a recent convolutional architecture for static point clouds. The
model also easily incorporates the notion of multiple entities in the process.
In particular, the model can flexibly answer prediction queries about arbitrary
space-time points for different entities regardless of the distribution of the
training or test-time data. We present experiments on real-world weather
station data and battles between large armies in StarCraft II. The results
demonstrate the model's flexibility in answering a variety of query types and
demonstrate improved performance and efficiency compared to state-of-the-art
baselines.

本论文提出了一种使用深度学习模型对不规则采样的连续空间时间过程进行直接学习和预测的方法，并验证了该模型在真实天气数据和星际争霸 II 中的有效性。