Previous approaches to generate shapes in a 3D setting train a GAN on the
latent space of an autoencoder (AE). Even though this produces convincing
results, it has two major shortcomings. As the GAN is limited to reproduce the
dataset the AE was trained on, we cannot reuse a trained AE for novel data.
Furthermore, it is difficult to add spatial supervision into the generation
process, as the AE only gives us a global representation. To remedy these
issues, we propose to train the GAN on grids (i.e. each cell covers a part of a
shape). In this representation each cell is equipped with a latent vector
provided by an AE. This localized representation enables more expressiveness
(since the cell-based latent vectors can be combined in novel ways) as well as
spatial control of the generation process (e.g. via bounding boxes). Our method
outperforms the current state of the art on all established evaluation
measures, proposed for quantitatively evaluating the generative capabilities of
GANs. We show limitations of these measures and propose the adaptation of a
robust criterion from statistical analysis as an alternative.

该研究提出了一种使用网格训练 GAN 的方法来生成三维形状，这种训练方法允许更多的表现力和空间控制，并提出了一种基于统计分析的鲁棒性评估标准。

基于网格的隐式函数的 3D 形状生成

3D Shape Generation with Grid-based Implicit Functions

3D shape generation is a challenging problem due to the high-dimensional
output space and complex part configurations of real-world objects. As a
result, existing algorithms experience difficulties in accurate generative
modeling of 3D shapes. Here, we propose a novel factorized generative model for
3D shape generation that sequentially transitions from coarse to fine scale
shape generation. To this end, we introduce an unsupervised primitive discovery
algorithm based on a higher-order conditional random field model. Using the
primitive parts for shapes as attributes, a parameterized 3D representation is
modeled in the first stage. This representation is further refined in the next
stage by adding fine scale details to shape. Our results demonstrate improved
representation ability of the generative model and better quality samples of
newly generated 3D shapes. Further, our primitive generation approach can
accurately parse common objects into a simplified representation.

本文提出了一种基于分解的生成模型，通过引入无监督的原始发现算法和基于高阶条件随机场模型的生成模型，可以从粗到细先后进行三维形状的生成，从而实现了对新形状生成表示能力的提高和更好的样本质量。