Implicit neural representation is a recent approach to learn shape collections as zero level-sets of neural networks, where each shape is represented by a latent code. So far, the focus has been shape reconstruction, while shape generalization was mostly left to generic encoder-decoder or auto-decoder regularization. In this paper we advocate deformation-aware regularization for implicit neural representations, aiming at producing plausible deformations as latent code changes. The challenge is that implicit representations do not capture correspondences between different shapes, which makes it difficult to represent and regularize their deformations. Thus, we propose to pair the implicit representation of the shapes with an explicit, piecewise linear deformation field, learned as an auxiliary function. We demonstrate that, by regularizing these deformation fields, we can encourage the implicit neural representation to induce natural deformations in the learned shape space, such as as-rigid-as-possible deformations.
翻译:隐性神经代言是最近的一种方法,将形状收藏作为神经网络的零层数据集来学习,每个形状都以潜伏代码为代表。 到目前为止,焦点一直是形状重建,而形状一般化则主要留给普通的编码解码器或自动解码器规范化。在本文中,我们主张对隐含神经代言进行变形-觉悟规范化,目的是作为潜伏代码变化而产生貌似变形。挑战在于隐含的代言无法捕捉不同形状之间的对应关系,因此难以代表并规范其变形。因此,我们提议将形状的隐含代言与作为辅助功能而学习的清晰的、有条形线变形字段相配对。我们证明,通过将这些变形字段的正规化,我们可以鼓励隐含的神经代言,以诱导出在有知识的形状空间(例如可变的变形)中发生自然变形,例如硬化的变形。