Advancing Structured Priors for Sparse-Voxel Surface Reconstruction

• URL: http://arxiv.org/abs/2601.17720v1
• Date: Sun, 25 Jan 2026 06:49:22 GMT
• Title: Advancing Structured Priors for Sparse-Voxel Surface Reconstruction
• Abstract summary: Two promising explicit representations, 3D Gaussian Splatting and sparse-voxelization, exhibit complementary strengths and weaknesses.<n>We combine the advantages of both by a voxel method that places voxels at plausible locations and with appropriate levels of detail.<n>Experiments on standard benchmarks demonstrate improvements over prior methods in accuracy, better fine-structure recovery, and more complete surfaces.
• Score: 38.315369778574386
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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