We introduce a Progressive Positional Encoding (PPE) layer, which gradually exposes signals with increasing frequencies throughout the neural optimization. In this paper, we show the competence of the PPE layer for mesh transfer and its advantages compared to contemporary surface mapping techniques. Our approach is simple and requires little user guidance. Most importantly, our technique is a parameterization-free method, and thus applicable to a variety of target shape representations, including point clouds, polygon soups, and non-manifold meshes. We demonstrate that the transferred meshing remains faithful to the source mesh design characteristics, and at the same time fits the target geometry well.

中文翻译：

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神经优化的渐进编码

我们引入了渐进式位置编码（PPE）层，该层在整个神经优化过程中逐渐暴露出频率越来越高的信号。在本文中，我们展示了PPE层在网格传输方面的能力及其与现代表面贴图技术相比的优势。我们的方法很简单，几乎不需要用户指导。最重要的是，我们的技术是一种无需参数化的方法，因此可应用于各种目标形状表示，包括点云，多边形汤和非流形网格。我们证明了转移的网格划分仍然忠实于源网格设计特征，同时又很好地适合了目标几何形状。