Discrete Point Flow Networks for Efficient Point Cloud Generation
This work addresses the problem of generating 3D point clouds efficiently for computer vision applications, representing an incremental improvement over existing methods.
The paper tackles efficient point cloud generation by introducing a latent variable model based on normalizing flows with affine coupling layers, achieving improved performance over GAN-based models and significant speedups compared to continuous flows while matching state-of-the-art results in single-view shape reconstruction.
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.