This work presents a generative adversarial architecture for generating
three-dimensional shapes based on signed distance representations. While the
deep generation of shapes has been mostly tackled by voxel and surface point
cloud approaches, our generator learns to approximate the signed distance for
any point in space given prior latent information. Although structurally
similar to generative point cloud approaches, this formulation can be evaluated
with arbitrary point density during inference, leading to fine-grained details
in generated outputs. Furthermore, we study the effects of using either
progressively growing voxel- or point-processing networks as discriminators,
and propose a refinement scheme to strengthen the generator's capabilities in
modeling the zero iso-surface decision boundary of shapes. We train our
approach on the ShapeNet benchmark dataset and validate, both quantitatively
and qualitatively, its performance in generating realistic 3D shapes.

本研究提出了一种基于有符号距离表示的生成对抗神经网络架构，能够生成高保真度的三维模型，并通过使用不同的 progressively growing voxel 网络作为鉴别器以及细化生成器能力的算法，从 ShapeNet 基准数据集中验证了其有效性。