论文标题
离散点流网络，用于有效的点云生成
Discrete Point Flow Networks for Efficient Point Cloud Generation
论文作者
论文摘要
事实证明，生成模型在建模3D形状及其统计变化方面有效。在本文中，我们调查了它们在点云中的应用，这是计算机视觉中广泛使用的3D形状表示，但是，仅提出了很少的生成模型。我们引入了一个潜在变量模型，该模型基于使用仿射耦合层标准化流量，以生成带有潜在形状表示的任意大小的3D点云。为了评估其形状建模的好处，我们将此模型应用于生成，自动编码和单视形状重建任务。从评估生成和自动编码的大多数指标方面，我们对最近的基于GAN的模型有所改善。与基于连续流的最新工作相比，我们的模型在培训和推理时间均具有相似或更好的性能的速度。对于单视形状重建，我们还可以通过最新的体素，点云和基于网格的方法获得结果。
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.
