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Recently normalizing flows (NFs) have demonstrated state-of-the-artperformance on modeling 3D point clouds while allowing sampling with arbitraryresolution at inference time. However, these flow-based models still requirelong training times and large models for representing complicated geometries.This work enhances their representational power by applying mixtures of NFs topoint clouds. We show that in this more general framework each component learnsto specialize in a particular subregion of an object in a completelyunsupervised fashion. By instantiating each mixture component with acomparatively small NF we generate point clouds with improved details comparedto single-flow-based models while using fewer parameters and considerablyreducing the inference runtime. We further demonstrate that by adding dataaugmentation, individual mixture components can learn to specialize in asemantically meaningful manner. We evaluate mixtures of NFs on generation,autoencoding and single-view reconstruction based on the ShapeNet dataset.

Go with the Flows: Mixtures of Normalizing Flows for Point Cloud Generation and Reconstruction