We propose DeMapGS, a structured Gaussian Splatting framework that jointly optimizes deformable surfaces and surface-attached 2D Gaussian splats. By anchoring splats to a deformable template mesh, our method overcomes topological inconsistencies and enhances editing flexibility, addressing limitations of prior Gaussian Splatting methods that treat points independently. The unified representation in our method supports extraction of high-fidelity diffuse, normal, and displacement maps, enabling the reconstructed mesh to inherit the photorealistic rendering quality of Gaussian Splatting. To support robust optimization, we introduce a gradient diffusion strategy that propagates supervision across the surface, along with an alternating 2D/3D rendering scheme to handle concave regions. Experiments demonstrate that DeMapGS achieves state-of-the-art mesh reconstruction quality and enables downstream applications for Gaussian splats such as editing and cross-object manipulation through a shared parametric surface.
翻译:我们提出了DeMapGS,这是一个结构化的高斯泼溅框架,能够联合优化可变形表面与表面附着的二维高斯泼溅。通过将泼溅锚定到可变形模板网格上,我们的方法克服了拓扑不一致性问题,并增强了编辑灵活性,从而解决了先前高斯泼溅方法中独立处理点所存在的局限性。我们方法中的统一表示支持提取高保真度的漫反射贴图、法线贴图和位移贴图,使得重建的网格能够继承高斯泼溅的光照真实感渲染质量。为了支持稳健的优化,我们引入了一种梯度扩散策略,该策略在表面上传播监督信号,并结合交替的二维/三维渲染方案来处理凹面区域。实验表明,DeMapGS实现了最先进的网格重建质量,并通过共享参数化表面,支持高斯泼溅的下游应用,如编辑和跨对象操作。