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Point clouds, as a form of Lagrangian representation, allow for powerful andflexible applications in a large number of computational disciplines. Wepropose a novel deep-learning method to learn stable and temporally coherentfeature spaces for points clouds that change over time. We identify a set ofinherent problems with these approaches: without knowledge of the timedimension, the inferred solutions can exhibit strong flickering, and easysolutions to suppress this flickering can result in undesirable local minimathat manifest themselves as halo structures. We propose a novel temporal lossfunction that takes into account higher time derivatives of the pointpositions, and encourages mingling, i.e., to prevent the aforementioned halos.We combine these techniques in a super-resolution method with a truncationapproach to flexibly adapt the size of the generated positions. We show thatour method works for large, deforming point sets from different sources todemonstrate the flexibility of our approach.

Tranquil Clouds: Neural Networks for Learning Temporally Coherent Features in Point Clouds