Neural implicit surface representations have recently emerged as popular alternative to explicit 3D object encodings, such as polygonal meshes, tabulated points, or voxels. While significant work has improved the geometric fidelity of these representations, much less attention is given to their final appearance. Traditional explicit object representations commonly couple the 3D shape data with auxiliary surface-mapped image data, such as diffuse color textures and fine-scale geometric details in normal maps that typically require a mapping of the 3D surface onto a plane, i.e., a surface parameterization; implicit representations, on the other hand, cannot be easily textured due to lack of configurable surface parameterization. Inspired by this digital content authoring methodology, we design a neural network architecture that implicitly encodes the underlying surface parameterization suitable for appearance data. As such, our model remains compatible with existing mesh-based digital content with appearance data. Motivated by recent work that overfits compact networks to individual 3D objects, we present a new weight-encoded neural implicit representation that extends the capability of neural implicit surfaces to enable various common and important applications of texture mapping. Our method outperforms reasonable baselines and state-of-the-art alternatives.

翻译：神经隐式表面表示作为多边形网格、离散点或体素等显式3D物体编码的替代方案，近年来日益受到关注。尽管大量研究致力于提升这些表示的几何保真度，但其最终外观方面却较少受到重视。传统的显式物体表示通常将3D形状数据与辅助的表面映射图像数据（如漫反射颜色纹理和法线贴图中的精细几何细节）相结合——这通常需要将3D表面映射到平面，即表面参数化；而隐式表示由于缺乏可配置的表面参数化，难以直接进行纹理映射。受这一数字内容创作方法的启发，我们设计了一种神经架构，能够隐式编码适用于外观数据的底层表面参数化。因此，我们的模型仍可兼容现有基于网格且包含外观数据的数字内容。受近期将紧凑网络过拟合至单个3D对象的研究启发，我们提出了一种新的权重编码神经隐式表示，该表示扩展了神经隐式表面的能力，能够实现纹理映射中多种常见且重要的应用。我们的方法在性能上优于合理的基线方法和当前最先进的替代方案。