Reconstructing accurate surfaces with radiance fields has progressed rapidly, yet two promising explicit representations, 3D Gaussian Splatting and sparse-voxel rasterization, exhibit complementary strengths and weaknesses. 3D Gaussian Splatting converges quickly and carries useful geometric priors, but surface fidelity is limited by its point-like parameterization. Sparse-voxel rasterization provides continuous opacity fields and crisp geometry, but its typical uniform dense-grid initialization slows convergence and underutilizes scene structure. We combine the advantages of both by introducing a voxel initialization method that places voxels at plausible locations and with appropriate levels of detail, yielding a strong starting point for per-scene optimization. To further enhance depth consistency without blurring edges, we propose refined depth geometry supervision that converts multi-view cues into direct per-ray depth regularization. Experiments on standard benchmarks demonstrate improvements over prior methods in geometric accuracy, better fine-structure recovery, and more complete surfaces, while maintaining fast convergence.

翻译：基于辐射场的精确表面重建技术发展迅速，然而两种前景广阔的显式表示方法——3D Gaussian Splatting与稀疏体素栅格化——呈现出互补的优势与不足。3D Gaussian Splatting收敛速度快且具备有效的几何先验，但其类点参数化限制了表面保真度。稀疏体素栅格化能提供连续的不透明度场与清晰的几何结构，但其典型的均匀密集网格初始化方式会减缓收敛速度且未能充分利用场景结构。我们通过引入一种体素初始化方法融合了两者的优势：该方法将体素置于合理位置并赋予适当的细节层次，从而为单场景优化提供了优质的初始状态。为进一步提升深度一致性同时避免边缘模糊，我们提出改进的深度几何监督机制，将多视角线索转化为直接的光线级深度正则化。在标准基准测试上的实验表明，本方法在几何精度、细粒度结构恢复和表面完整性方面均优于现有方法，同时保持了快速收敛特性。