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.

翻译：近年来，已出现多种利用神经网络以部件化表示形式合成三维形状的方法。这些方法将形状表示为部件构成的图结构或层次结构，支持形状采样与重建等多种应用。然而，现有方法难以根据用户偏好便捷地重新生成单个形状部件。本文研究允许用户为单个部件生成多种多样性建议的技术。具体而言，我们通过多模态深度生成模型实验实现对形状部件的多样性建议采样，重点关注先前形状合成研究中未涉及的模型。为对这些技术进行对比研究，我们提出一种基于部件表示的三维形状合成方法，并在该合成框架内评估所有部件建议生成技术。受前人工作启发，我们的方法将形状表示为隐函数形式的部件集合，通过空间排布形成最终形状。基于隐式解码器与空间变换器的神经网络架构实现了该表示下的形状合成。我们通过评估各多模态生成模型在部件建议生成中的表现进行对比研究。本研究的贡献在于：通过定性与定量评估揭示了哪些新型多模态部件生成技术性能最优，并证明基于最优技术的合成方法能够在保持高保真形状重建的同时，让用户更精细地控制三维形状中生成的部件。