三维形状创建的多样部件合成
Diverse Part Synthesis for 3D Shape Creation
AI总结：
本文提出一种基于部件表示的三维形状合成方法，并比较多种多模态深度生成模型，以支持用户对单个部件进行多样建议生成，实现精细控制并保持高保真度。
摘要：
近年来，已有方法利用神经网络以基于部件的表示形式合成三维形状。这些方法将形状表示为部件图或部件层级结构，并支持多种应用，如形状采样和重建。然而，当前方法不允许根据用户偏好轻松地重新生成单个形状部件。在本文中，我们研究了允许用户为单个部件生成多个多样建议的技术。具体来说，我们实验了多模态深度生成模型，这些模型允许对形状部件进行多样建议的采样，并重点关注以往形状合成工作中未考虑的模型。为了对这些技术进行比较研究，我们提出了一种以基于部件的表示形式合成三维形状的方法，并在该合成方法中评估所有部件建议技术。在我们的方法中，受先前工作的启发，形状被表示为一系列隐式函数形式的部件，这些部件随后在空间中定位以形成最终形状。这种表示中的合成由基于隐式解码器和空间变换器的神经网络架构实现。我们通过评估各种多模态生成模型在生成部件建议方面的性能来比较它们。我们的贡献在于通过定性和定量评估展示，哪些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允许用户更精细地控制三维形状中生成的部件，同时在重建形状时保持高形状保真度。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
