Structural representation is crucial for reconstructing and generating editable 3D shapes with part semantics. Recent 3D shape generation works employ complicated networks and structure definitions relying on hierarchical annotations and pay less attention to the details inside parts. In this paper, we propose the method that parameterizes the shared structure in the same category using a differentiable template and corresponding fixed-length parameters. Specific parameters are fed into the template to calculate cuboids that indicate a concrete shape. We utilize the boundaries of three-view drawings of each cuboid to further describe the inside details. Shapes are represented with the parameters and three-view details inside cuboids, from which the SDF can be calculated to recover the object. Benefiting from our fixed-length parameters and three-view details, our networks for reconstruction and generation are simple and effective to learn the latent space. Our method can reconstruct or generate diverse shapes with complicated details, and interpolate them smoothly. Extensive evaluations demonstrate the superiority of our method on reconstruction from point cloud, generation, and interpolation.

翻译：结构表示对于重建和生成具有部件语义的可编辑三维形状至关重要。现有的三维形状生成方法采用复杂的网络和依赖于层次化标注的结构定义，且较少关注部件内部的细节。本文提出一种方法，利用可微分模板和对应的定长参数对同一类别中的共享结构进行参数化。将具体参数输入模板以计算指示特定形状的立方体。我们利用每个立方体三视图的边界进一步描述内部细节。形状通过参数和立方体内的三视图细节表示，并可通过计算SDF从中恢复物体。得益于定长参数和三视图细节，我们用于重建和生成的网络结构简单且能有效学习潜在空间。本方法能够重建或生成具有复杂细节的多样化形状，并实现平滑插值。大量实验评估证明了本方法在点云重建、生成和插值任务上的优越性。