Inspired by the ability of water to assimilate any shape, if being poured into it, regardless if flat, round, sharp, or pointy, we present a novel, high-quality meshing method. Our algorithm creates a triangulated mesh, which automatically refines where necessary and accurately aligns to any target, given as mesh, point cloud, or volumetric function. Our core optimization iterates over steps for mesh uniformity, point cloud projection, and mesh topology corrections, always guaranteeing mesh integrity and \(\epsilon \)-close surface reconstructions. In contrast with similar approaches, our simple algorithm operates on an individual vertex basis. This allows for automated and seamless transitions between the optimization phases for rough shape approximation and fine detail reconstruction. Therefore, our proposed algorithm equals established techniques in terms of accuracy and robustness but supersedes them in terms of simplicity and better feature reconstruction, all controlled by a single parameter, the intended edge length. Due to the overall increased versatility of input scenarios and robustness of the assimilation, our technique furthermore generalizes multiple established approaches such as ballooning or shrink wrapping.

受水同化任何形状的能力的启发，如果将其倒入其中，无论是扁平的、圆形的、尖锐的还是尖尖的，我们提出了一种新颖、高质量的网格划分方法。我们的算法会创建一个三角网格，它会在必要时自动优化并准确对齐任何目标，以网格、点云或体积函数的形式给出。我们的核心优化迭代网格均匀性、点云投影和网格拓扑校正的步骤，始终保证网格完整性和\(\epsilon \)- 关闭表面重建。与类似方法相比，我们的简单算法在单个顶点的基础上运行。这允许在粗略形状近似和精细细节重建的优化阶段之间实现自动化和无缝过渡。因此，我们提出的算法在准确性和鲁棒性方面等同于现有技术，但在简单性和更好的特征重建方面取代了它们，所有这些都由单个参数控制，即预期的边缘长度。由于输入场景的通用性和同化的鲁棒性的整体增加，我们的技术进一步推广了多种已建立的方法，例如气球或收缩包装。