Partitioning a polygonal mesh into meaningful parts can be challenging. Many applications require decomposing such structures for further processing in computer graphics. In the last decade, several methods were proposed to tackle this problem, at the cost of intensive computational times. Recently, machine learning has proven to be effective for the segmentation task on 3D structures. Nevertheless, these state-of-the-art methods are often hardly generalizable and require dividing the learned model into several specific classes of objects to avoid overfitting. We present a data-driven approach leveraging deep learning to encode a mapping function prior to mesh segmentation for multiple applications. Our network reproduces a neighborhood map using our knowledge of the \textsl{Shape Diameter Function} (SDF) method using similarities among vertex neighborhoods. Our approach is resolution-agnostic as we downsample the input meshes and query the full-resolution structure solely for neighborhood contributions. Using our predicted SDF values, we can inject the resulting structure into a graph-cut algorithm to generate an efficient and robust mesh segmentation while considerably reducing the required computation times.

翻译：将多边形网格分割成有意义的部件充满挑战。许多应用需要分解此类结构以进行计算机图形学中的后续处理。过去十年中，研究者提出了多种方法来解决该问题，但代价是计算时间较长。近年来，机器学习在三维结构分割任务中已证明其有效性。然而，这些先进方法通常难以泛化，需要将所学模型划分为多个特定物体类别以避免过拟合。我们提出一种数据驱动方法，利用深度学习在网格分割前编码映射函数，适用于多种应用场景。我们的网络利用我们对基于顶点邻域相似性的\textsl{形状直径函数}（SDF）方法的知识，再现邻域图。由于我们对输入网格进行降采样，并仅针对邻域贡献查询全分辨率结构，因此我们的方法与分辨率无关。利用预测的SDF值，我们将所得结构注入图割算法，从而在显著降低计算时间的同时生成高效且鲁棒的网格分割。