LeafFit: Plant Assets Creation from 3D Gaussian Splatting

Abstract

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.

Workflow

(a) Input: a pre-trained set of Gaussian primitives $\mathcal{G}$. (b) The user selects a root primitive $r$; we compute geodesic distances from $r$, detect apexes $a$ as local maxima, and build a tree. The tree graph is used to find leaf petiole. (c) Using petiole and apex cues, we segment leaves and let the user choose a template leaf $\mathcal{L}_t$. (d) Other leaves are fitted to $\mathcal{L}_t$ via Moving Least Squares deformation. (e) Output: an efficient, editable instanced mesh suitable for game assets.