Go with the Flows: Mixtures of Normalizing Flows for Point Cloud Generation and Reconstruction
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Date
2021
Publication Type
Conference Paper
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yes
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Abstract
Recently Normalizing Flows (NFs) have demonstrated state-of-the-art performance on modeling 3D point clouds while allowing sampling with arbitrary resolution at inference time. However, these flow-based models still have fundamental limitations on complicated geometries. This work generalizes prior work by introducing additional discrete latent variable, i.e. mixture model. This circumvents limitations of prior approaches, leads to more parameter efficient models and reduces the inference runtime. Moreover, in this more general framework each component learns to specialize in a particular subregion of an object in a completely unsupervised fashion yielding promising clustering properties. We further demonstrate that by adding data augmentation, individual mixture components can learn to specialize in a semantically meaningful manner. We evaluate mixtures of NFs on generation, autoencoding and single-view reconstruction based on the ShapeNet dataset.
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published
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Book title
2021 International Conference on 3D Vision (3DV)
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Volume
Pages / Article No.
1249 - 1258
Publisher
IEEE
Event
9th International Conference on 3D Vision (3DV 2021)
Edition / version
Methods
Software
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Date collected
Date created
Subject
Generative models; Normalizing flows; Point clouds; Single view reconstruction
Organisational unit
03514 - Van Gool, Luc (emeritus) / Van Gool, Luc (emeritus)