Parameterize Structure with Differentiable Template for 3D Shape Generation.

Changfeng Ma, Pengxiao Guo, Shuangyu Yang, Yinuo Chen, Jie Guo, Chongjun Wang, Yanwen Guo, Wenping Wang
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Abstract

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 renderings 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.

用可微模板参数化结构用于三维形状生成。
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。目前的三维形状生成工作采用复杂的网络和结构定义,依赖于分层注释,对零件内部细节的关注较少。本文提出了一种利用可微模板和相应的定长参数对同一范畴内的共享结构进行参数化的方法。将特定参数输入到模板中,以计算长方体,表示具体形状。我们利用每个长方体的三视图渲染的边界来进一步描述内部细节。形状用长方体内部的参数和三视图细节来表示,从中可以计算SDF来恢复物体。利用定长参数和三视图细节,我们的重建和生成网络简单有效地学习了潜在空间。该方法可以重建或生成具有复杂细节的多种形状,并实现平滑的插值。广泛的评估证明了我们的方法在点云重建、生成和插值方面的优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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