  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.
