We propose LeafFit, a pipeline that converts 3D Gaussian Splatting (3DGS) of individual plants into editable, instanced mesh assets. While 3DGS faithfully captures complex foliage, its high memory footprint and lack of mesh topology make it incompatible with traditional game production workflows. We address this by leveraging the repetition of leaf shapes; our method segments leaves from the unstructured 3DGS, with optional user interaction included as a fallback. A representative leaf group is selected and converted into a thin, sharp mesh to serve as a template; this template is then fitted to all other leaves via differentiable Moving Least Squares (MLS) deformation. At runtime, the deformation is evaluated efficiently on-the-fly using a vertex shader to minimize storage requirements. Experiments demonstrate that LeafFit achieves higher segmentation quality and deformation accuracy than recent baselines while significantly reducing data size and enabling parameter-level editing.

翻译：本文提出LeafFit，一种将单株植物的3D高斯泼溅（3DGS）转换为可编辑、可实例化网格资产的技术流程。尽管3DGS能够精确捕捉复杂的叶片结构，但其高内存占用与缺乏网格拓扑的特性使其难以融入传统游戏生产流程。我们通过利用叶片形状的重复性来解决这一问题：该方法从非结构化的3DGS中分割叶片，并包含可选的用户交互作为备用方案。选取具有代表性的叶片群组并转换为薄锐网格作为模板，随后通过可微分移动最小二乘（MLS）变形将该模板适配至所有其他叶片。在运行时，通过顶点着色器实时高效计算变形量以最小化存储需求。实验表明，相较于现有基线方法，LeafFit在显著降低数据量并支持参数级编辑的同时，实现了更优的分割质量与变形精度。