Deep Convolution for Irregularly Sampled Temporal Point Clouds
It addresses the problem of handling irregularly sampled data in applications like sensor networks and multi-robot systems, offering a flexible solution for arbitrary queries, though it builds on existing point cloud methods.
The paper tackles modeling dynamics of continuous spatial-temporal processes from irregular samples by proposing a deep model that directly learns and predicts without voxelization, showing improved performance and efficiency on real-world weather and StarCraft II data.
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.