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具有挑战性。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。过去十年间，研究者提出多种方法解决该问题，但均以高计算时间为代价。近年来，机器学习已被证明能有效完成三维结构分割任务。然而，这些先进方法往往难以泛化，需将学习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为多应用场景编码映射函数。我们的网络通过利用顶点邻域间的相似性，基于形状直径函数方法的知识复现邻域映射。该方法具有分辨率无关性：我们对输入网格进行降采样，仅查询全分辨率结构的邻域贡献。通过预测的SDF值，将生成的结构注入图割算法，可在显著降低计算时间的同时实现高效鲁棒的网格分割。