In this work, we present a new learning-based pipeline for the generation of 3D shapes. We build our method on top of recent advances on the so called shape-from-spectrum paradigm, which aims at recovering the full 3D geometric structure of an object only from the eigenvalues of its Laplacian operator. In designing our learning strategy, we consider the spectrum as a natural and ready to use representation to encode variability of the shapes. Therefore, we propose a simple decoder-only architecture that directly maps spectra to 3D embeddings; in particular, we combine information from global and local spectra, the latter being obtained from localized variants of the manifold Laplacian. This combination captures the relations between the full shape and its local parts, leading to more accurate generation of geometric details and an improved semantic control in shape synthesis and novel editing applications. Our results confirm the improvement of the proposed approach in comparison to existing and alternative methods.

翻译：在这项工作中,我们提出了一个新的基于学习的3D形状生成管道。我们在所谓的光谱形状范式最新进展的基础上,又建立了我们的方法,目的是从一个物体的拉巴拉西亚操作员的外形价值中完全恢复一个物体的全部 3D 几何结构。在设计我们的学习战略时,我们认为频谱是自然的,可以使用演示来编码形状的变异性。因此,我们提议了一个简单的只用解码器来直接绘制光谱到 3D 嵌入器的架构;特别是,我们把来自全球和本地光谱的光谱和当地光谱的信息结合起来,而后者是从多功能的本地变异体中获取的。这种组合可以捕捉到整个形状及其局部部分之间的关系,导致更准确地生成几何细节,并改进形状合成和新编辑应用中的语法控制。我们的结果证实了与现有和替代方法相比,拟议方法的改进。