使用表面信号参数化学习神经隐式表示
Learning Neural Implicit Representations with Surface Signal Parameterizations
AI总结：
提出一种权重编码神经隐式表示方法，通过隐式编码表面参数化实现纹理映射，使隐式表面兼容现有网格数字内容，并在性能上超越基线与现有先进方法。
摘要：
神经隐式表面表示最近作为显式3D对象编码（如多边形网格、表格化点或体素）的流行替代方案而出现。虽然已有大量工作提升了这些表示的几何保真度，但对其最终外观的关注却少得多。传统的显式对象表示通常将3D形状数据与辅助的表面映射图像数据结合，例如漫反射颜色纹理和法线贴图中的精细几何细节，这通常需要将3D表面映射到平面上，即表面参数化；另一方面，由于缺乏可配置的表面参数化，隐式表示无法轻易进行纹理映射。受这种数字内容创作方法的启发，我们设计了一种神经网络架构，隐式编码适合外观数据的底层表面参数化。因此，我们的模型与基于网格的带有外观数据的现有数字内容保持兼容。受近期将紧凑网络过拟合到单个3D对象的工作启发，我们提出了一种新的权重编码神经隐式表示，扩展了神经隐式表面的能力，以实现纹理映射的各种常见且重要的应用。我们的方法优于合理的基线和最先进的替代方案。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
