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.

翻译：现有UV映射算法主要针对结构规整的网格设计，而非适用于当前最先进的3D重建与生成技术所输出的几何表示。因此，将这些方法应用于神经辐射场及相关技术恢复的体积密度（或从该场域三角化得到的网格），会导致纹理图集过于碎片化，难以用于视图合成或外观编辑等任务。我们提出一种专为3D重建与生成技术产生的几何结构设计的UV映射方法。该方法Nuvo无需计算网格顶点上的映射关系，而是采用神经场表征连续UV映射，并针对仅影响场景外观的可见点集，优化生成有效且规整的映射。实验表明，我们的模型能够有效应对非规整几何结构带来的挑战，并生成可编辑的UV映射，实现细节化的外观表征。