Nuvo：面向不规则3D表示的神经UV映射
Nuvo: Neural UV Mapping for Unruly 3D Representations
AI总结：
针对神经辐射场等产生的不规则3D几何，本文提出Nuvo，用神经场表示并优化仅覆盖可见点的连续UV映射，以获得鲁棒、可编辑且能呈现细致外观的纹理坐标。
摘要：
现有UV映射算法被设计用于处理表现良好的网格，而非由最先进的3D重建和生成技术产生的几何表示。因此，将这些方法应用于由神经辐射场及相关技术恢复的体密度（或从此类场三角化得到的网格）时，会产生过于碎片化的纹理图集，无法用于视图合成或外观编辑等任务。我们提出了一种UV映射方法，专门用于处理由3D重建和生成技术产生的几何。不同于计算定义在网格顶点上的映射，我们的方法Nuvo使用一个神经场来表示连续的UV映射，并将其优化为仅针对可见点集合（即仅影响场景外观的点）的有效且表现良好的映射。我们证明，我们的模型对不良几何带来的挑战具有鲁棒性，并且能够生成可编辑的UV映射，这些映射可以表示细致的外观。
英文摘要：
Existing UV mapping algorithms are designed to operate on well-behaved meshes, instead of the geometry representations produced by state-of-the-art 3D reconstruction and generation techniques. As such, applying these methods to the volume densities recovered by neural radiance fields and related techniques (or meshes triangulated from such fields) results in texture atlases that are too fragmented to be useful for tasks such as view synthesis or appearance editing. We present a UV mapping method designed to operate on geometry produced by 3D reconstruction and generation techniques. Instead of computing a mapping defined on a mesh's vertices, our method Nuvo uses a neural field to represent a continuous UV mapping, and optimizes it to be a valid and well-behaved mapping for just the set of visible points, i.e. only points that affect the scene's appearance. We show that our model is robust to the challenges posed by ill-behaved geometry, and that it produces editable UV mappings that can represent detailed appearance.
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