Discrete Point Flow Networks for Efficient Point Cloud Generation

• URL: http://arxiv.org/abs/2007.10170v1
• Date: Mon, 20 Jul 2020 14:48:00 GMT
• Title: Discrete Point Flow Networks for Efficient Point Cloud Generation
• Authors: Roman Klokov, Edmond Boyer, Jakob Verbeek
• Abstract summary: Generative models have proven effective at modeling 3D shapes and their statistical variations. We introduce a latent variable model that builds on normalizing flows to generate 3D point clouds of an arbitrary size. For single-view shape reconstruction we also obtain results on par with state-of-the-art voxel, point cloud, and mesh-based methods.
• Score: 36.03093265136374
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative models have proven effective at modeling 3D shapes and their statistical variations. In this paper we investigate their application to point clouds, a 3D shape representation widely used in computer vision for which, however, only few generative models have yet been proposed. We introduce a latent variable model that builds on normalizing flows with affine coupling layers to generate 3D point clouds of an arbitrary size given a latent shape representation. To evaluate its benefits for shape modeling we apply this model for generation, autoencoding, and single-view shape reconstruction tasks. We improve over recent GAN-based models in terms of most metrics that assess generation and autoencoding. Compared to recent work based on continuous flows, our model offers a significant speedup in both training and inference times for similar or better performance. For single-view shape reconstruction we also obtain results on par with state-of-the-art voxel, point cloud, and mesh-based methods.

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