  Existing generative models for 3D shapes are typically trained on a large 3D
dataset, often of a specific object category. In this paper, we investigate the
deep generative model that learns from only a single reference 3D shape.
Specifically, we present a multi-scale GAN-based model designed to capture the
input shape's geometric features across a range of spatial scales. To avoid
large memory and computational cost induced by operating on the 3D volume, we
build our generator atop the tri-plane hybrid representation, which requires
only 2D convolutions. We train our generative model on a voxel pyramid of the
reference shape, without the need of any external supervision or manual
annotation. Once trained, our model can generate diverse and high-quality 3D
shapes possibly of different sizes and aspect ratios. The resulting shapes
present variations across different scales, and at the same time retain the
global structure of the reference shape. Through extensive evaluation, both
qualitative and quantitative, we demonstrate that our model can generate 3D
shapes of various types.
