Methods that use neural networks for synthesizing 3D shapes in the form of a
part-based representation have been introduced over the last few years. These
methods represent shapes as a graph or hierarchy of parts and enable a variety
of applications such as shape sampling and reconstruction. However, current
methods do not allow easily regenerating individual shape parts according to
user preferences. In this paper, we investigate techniques that allow the user
to generate multiple, diverse suggestions for individual parts. Specifically,
we experiment with multimodal deep generative models that allow sampling
diverse suggestions for shape parts and focus on models which have not been
considered in previous work on shape synthesis. To provide a comparative study
of these techniques, we introduce a method for synthesizing 3D shapes in a
part-based representation and evaluate all the part suggestion techniques
within this synthesis method. In our method, which is inspired by previous
work, shapes are represented as a set of parts in the form of implicit
functions which are then positioned in space to form the final shape. Synthesis
in this representation is enabled by a neural network architecture based on an
implicit decoder and a spatial transformer. We compare the various multimodal
generative models by evaluating their performance in generating part
suggestions. Our contribution is to show with qualitative and quantitative
evaluations which of the new techniques for multimodal part generation perform
the best and that a synthesis method based on the top-performing techniques
allows the user to more finely control the parts that are generated in the 3D
shapes while maintaining high shape fidelity when reconstructing shapes.

采用神经网络合成三维形状的方法中引入了基于部件表示的方法，在过去几年中已经出现。然而，当前方法不容易根据用户的偏好重新生成单个形状部件。本文研究了允许用户生成多个多样化部件建议的技术，特别是实验了先前的形状合成工作中未考虑的多模态深度生成模型。通过在基于部件表示的合成方法中评估这些技术，我们提供了一种比较研究。我们的贡献是定性和定量评估表明，哪种新的多模态部件生成技术表现最佳，并且基于表现最佳技术的合成方法在重构形状时可以更好地控制所生成的三维形状的部件，同时保持高形状保真度。

多元部分合成用于 3D 形状创建

Diverse Part Synthesis for 3D Shape Creation

We demonstrate a new deep learning autoencoder network, trained by a
nonnegativity constraint algorithm (NCAE), that learns features which show
part-based representation of data. The learning algorithm is based on
constraining negative weights. The performance of the algorithm is assessed
based on decomposing data into parts and its prediction performance is tested
on three standard image data sets and one text dataset. The results indicate
that the nonnegativity constraint forces the autoencoder to learn features that
amount to a part-based representation of data, while improving sparsity and
reconstruction quality in comparison with the traditional sparse autoencoder
and Nonnegative Matrix Factorization. It is also shown that this newly acquired
representation improves the prediction performance of a deep neural network.

本文展示了一种新的深度学习自编码器网络，该网络使用非负约束算法（NCAE）进行训练，学习到的特征显示出数据的部分表示。在三个标准图像数据集和一个文本数据集上测试了算法的性能，并发现非负约束强制自编码器学习到数据的部分表示，同时改善了与传统的稀疏自编码器和非负矩阵分解相比的稀疏性和重构质量，进而提高了深度神经网络的预测性能。