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.

翻译：我们提出了TetSphere splatting，一种显式的拉格朗日表示方法，用于重建具有高质量几何的三维形状。与主要采用欧拉表示（包括神经隐式表示如NeRF、NeuS，以及显式表示如DMTet）且常面临高计算需求和次优网格质量的传统物体重建方法不同，TetSphere splatting利用了一种未被充分利用但极为有效的几何基元——四面体网格。该方法无需依赖神经网络或后处理，即可直接生成优质的网格。它通过结合可微分渲染与几何能量优化，使多个初始四面体球体变形以精确重建三维形状，从而实现了显著的计算效率。作为一种鲁棒且通用的几何表示方法，Tet-Sphere splatting可无缝集成到多种应用中，包括单视图三维重建、图像/文本到三维内容生成。实验结果表明，TetSphere splatting优于现有表示方法，在优化速度更快、网格质量更高以及薄结构可靠保持方面表现突出。