用智能体素初始化提升稀疏体素表面重建精度与速度
Advancing Structured Priors for Sparse-Voxel Surface Reconstruction
- 基于场景结构智能布局体素，替代均匀初始化
- 多视角深度监督使几何更精确，边缘更清晰
- 兼顾快速收敛与细节还原，适合高保真三维重建
基于辐射场的表面重建技术发展迅速，但3D高斯泼溅与稀疏体素光栅化两种显式表示各有优劣。前者收敛快、具几何先验，但因点状参数化限制表面精度；后者提供连续不透明度场和锐利几何，却因均匀密集网格初始化导致收敛慢、未充分利用场景结构。本文提出一种体素初始化方法，将体素放置于合理位置并匹配合适细节层级，为每场景优化提供优质起点。为进一步增强深度一致性而不模糊边缘，设计了深度几何监督机制，将多视角线索转化为逐射线深度正则化。在标准基准上的实验表明，该方法在几何精度、细部恢复和表面完整性上均优于先前方法，同时保持快速收敛。
原文摘要 · Abstract (English)
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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