Mesh Processing Non-Meshes via Neural Displacement Fields
Yuta Noma and Zhecheng Wang and Chenxi Liu and Karan Singh and Alec Jacobson
网格处理流程已经成熟，但将其适配到更新的非网格表面表示——这些表示能够以紧凑的文件大小实现快速渲染——需要昂贵的网格化处理或传输庞大的网格数据，从而抵消了它们在流媒体应用中的核心优势。我们提出了一种紧凑的神经场，能够在不同的表面表示之间实现常见的几何处理任务。给定一个输入表面，我们的方法学习从其粗糙网格近似到表面的神经映射。完整的表示总共只有几百千字节，非常适合轻量级传输。我们的方法能够快速提取流形和Delaunay网格用于内蕴形状分析，并压缩标量场以高效交付昂贵的预计算结果。实验和应用表明，我们快速、紧凑且准确的方法为交互式几何处理开辟了新的可能性。
Mesh processing pipelines are mature, but adapting them to newer non-mesh surface representations – which enable fast rendering with compact file size – requires costly meshing or transmitting bulky meshes, negating their core benefits for streaming applications. We present a compact neural field that enables common geometry processing tasks across diverse surface representations. Given an input surface, our method learns a neural map from its coarse mesh approximation to the surface. The full r...