We propose an algorithm to reconstruct explicit polygonal meshes from discretely sampled Signed Distance Function (SDF) data, which is especially effective at recovering sharp features. Building on the traditional Dual Contouring of Hermite Data method, we design and solve a quadratic optimization problem to decide the optimal placement of the mesh's vertices within each cell of a regular grid. Critically, this optimization relies solely on discretely sampled SDF data, without requiring arbitrary access to the function, gradient information, or training on large-scale datasets. Our method sets a new state of the art in surface reconstruction from SDFs at medium and high resolutions, and opens the door for applications in 3D modeling and design.

翻译：我们提出一种从离散采样的有符号距离函数数据中重建显式多边形网格的算法，该算法在恢复尖锐特征方面尤为有效。基于传统的埃尔米特数据对偶等高线方法，我们设计并求解了一个二次优化问题，以确定规则网格每个单元内网格顶点的最优位置。关键在于，该优化过程仅依赖于离散采样的有符号距离函数数据，无需对函数本身进行任意访问、梯度信息或大规模数据集训练。我们的方法在中高分辨率下从有符号距离函数进行表面重建方面达到了当前最优水平，并为三维建模与设计领域的应用开辟了道路。