Recent advances in deep generative models have led to immense progress in 3D shape synthesis. While existing models are able to synthesize shapes represented as voxels, point-clouds, or implicit functions, these methods only indirectly enforce the plausibility of the final 3D shape surface. Here we present a 3D shape synthesis framework (SurfGen) that directly applies adversarial training to the object surface. Our approach uses a differentiable spherical projection layer to capture and represent the explicit zero isosurface of an implicit 3D generator as functions defined on the unit sphere. By processing the spherical representation of 3D object surfaces with a spherical CNN in an adversarial setting, our generator can better learn the statistics of natural shape surfaces. We evaluate our model on large-scale shape datasets, and demonstrate that the end-to-end trained model is capable of generating high fidelity 3D shapes with diverse topology.

通过直接应用对抗训练于对象表面的3D形状合成框架（SurfGen），使用可微分的球形投影层和球形CNN来更好地学习自然形状表面的统计数据，并在大规模形状数据集上评估和证明其能够生成具有多种拓扑结构的高保真3D形状。

SurfGen: 显式表面判别器的对抗性三维形状合成