3D generative shape modeling is a fundamental research area in computer vision and interactive computer graphics, with many real-world applications. This paper investigates the novel problem of generating 3D shape point cloud geometry from a symbolic part tree representation. In order to learn such a conditional shape generation procedure in an end-to-end fashion, we propose a conditional GAN "part tree"-to-"point cloud" model (PT2PC) that disentangles the structural and geometric factors. The proposed model incorporates the part tree condition into the architecture design by passing messages top-down and bottom-up along the part tree hierarchy. Experimental results and user study demonstrate the strengths of our method in generating perceptually plausible and diverse 3D point clouds, given the part tree condition. We also propose a novel structural measure for evaluating if the generated shape point clouds satisfy the part tree conditions.

本文介绍了一种从符号树表示生成 3D 点云几何形状的方法，提出了一种新的条件 GAN 模型（PT2PC），它将树部分作为条件并将其纳入架构设计中，以端到端方式学习形状生成程序，在用户研究中证明了本方法在生成感知合理和多样 3D 点云方面的优势，同时提出了一种用于评估生成形状跟符号树条件是否满足的新结构度量标准。

从部件结构条件中学习生成三维点云模型