We present TetSphere splatting, an explicit, Lagrangian representation for
reconstructing 3D shapes with high-quality geometry. In contrast to
conventional object reconstruction methods which predominantly use Eulerian
representations, including both neural implicit (e.g., NeRF, NeuS) and explicit
representations (e.g., DMTet), and often struggle with high computational
demands and suboptimal mesh quality, TetSphere splatting utilizes an underused
but highly effective geometric primitive -- tetrahedral meshes. This approach
directly yields superior mesh quality without relying on neural networks or
post-processing. It deforms multiple initial tetrahedral spheres to accurately
reconstruct the 3D shape through a combination of differentiable rendering and
geometric energy optimization, resulting in significant computational
efficiency. Serving as a robust and versatile geometry representation,
Tet-Sphere splatting seamlessly integrates into diverse applications, including
single-view 3D reconstruction, image-/text-to-3D content generation.
Experimental results demonstrate that TetSphere splatting outperforms existing
representations, delivering faster optimization speed, enhanced mesh quality,
and reliable preservation of thin structures.

我们提出了 TetSphere 喷洒，一种用于重建高质量几何形状的显式的拉格朗日表示。与传统的物体重建方法不同，它主要使用欧拉表示，包括神经隐式方法（如 NeRF，NeuS）和显式表示方法（如 DMTet），通常在高计算需求和低优化网格质量方面存在问题。 TetSphere 喷洒利用了一个被低估但非常有效的几何原语 - 四面体网格。通过可微渲染和几何能量优化的组合，它将多个初始四面体球体变形以准确重建 3D 形状，从而实现了显著的计算效率。作为一种强大而灵活的几何表示方法，Tet-Sphere 喷洒无缝地集成到各种应用中，包括基于单视角的 3D 重建和图像 / 文本到 3D 内容的生成。实验结果表明，TetSphere 喷洒优于现有的表示方法，具有更快的优化速度，增强的网格质量和可靠的薄结构保留。