Surface reconstruction with preservation of geometric features is a challenging computer vision task. Despite significant progress in implicit shape reconstruction, state-of-the-art mesh extraction methods often produce aliased, perceptually distorted surfaces and lack scalability to high-resolution 3D shapes. We present a data-driven approach for automatic feature detection and remeshing that requires only a coarse, aliased mesh as input and scales to arbitrary resolution reconstructions. We define and learn a collection of surface-based fields to (1) capture sharp geometric features in the shape with an implicit vertexwise model and (2) approximate improvements in normals alignment obtained by applying edge-flips with an edgewise model. To support scaling to arbitrary complexity shapes, we learn our fields using local triangulated patches, fusing estimates on complete surface meshes. Our feature remeshing algorithm integrates the learned fields as sharp feature priors and optimizes vertex placement and mesh connectivity for maximum expected surface improvement. On a challenging collection of high-resolution shape reconstructions in the ABC dataset, our algorithm improves over state-of-the-art by 26% normals F-score and 42% perceptual $\text{RMSE}_{\text{v}}$.

翻译：摘要：保持几何特征的曲面重建是一项具有挑战性的计算机视觉任务。尽管隐式形状重建取得了显著进展，但最先进的网格提取方法常产生锯齿状、感知扭曲的曲面，且难以扩展到高分辨率的三维形状。我们提出了一种数据驱动的自动特征检测与重网格化方法，该方法仅需粗糙的锯齿状网格作为输入，并可扩展至任意分辨率的重建。我们定义并学习一组基于表面的场：（1）通过隐式的逐顶点模型捕捉形状中的锐利几何特征；（2）通过逐边模型近似应用边翻转后法线对齐的改进效果。为支持扩展到任意复杂形状，我们利用局部三角化面片学习这些场，并在完整曲面网格上融合估计结果。我们的特征重网格化算法将学习到的场作为锐利特征先验，并优化顶点位置与网格连接性，以实现最大期望的曲面改进。在ABC数据集上一组高分辨率形状重建的挑战性集合中，我们的算法相较于最先进方法，法线F-score提升了26%，感知误差均方根（$\text{RMSE}_{\text{v}}$）降低了42%。