GeoSVR: Taming Sparse Voxels for Geometrically Accurate Surface
  Reconstruction
Authors: name: Jiahe Li, name: Jiawei Zhang, name: Youmin Zhang, name: Xiao Bai, name: Jin Zheng, name: Xiaohan Yu, name: Lin Gu

Reconstructing accurate surfaces with radiance fields has achieved remarkable
progress in recent years. However, prevailing approaches, primarily based on
Gaussian Splatting, are increasingly constrained by representational
bottlenecks. In this paper, we introduce GeoSVR, an explicit voxel-based
framework that explores and extends the under-investigated potential of sparse
voxels for achieving accurate, detailed, and complete surface reconstruction.
As strengths, sparse voxels support preserving the coverage completeness and
geometric clarity, while corresponding challenges also arise from absent scene
constraints and locality in surface refinement. To ensure correct scene
convergence, we first propose a Voxel-Uncertainty Depth Constraint that
maximizes the effect of monocular depth cues while presenting a voxel-oriented
uncertainty to avoid quality degradation, enabling effective and robust scene
constraints yet preserving highly accurate geometries. Subsequently, Sparse
Voxel Surface Regularization is designed to enhance geometric consistency for
tiny voxels and facilitate the voxel-based formation of sharp and accurate
surfaces. Extensive experiments demonstrate our superior performance compared
to existing methods across diverse challenging scenarios, excelling in
geometric accuracy, detail preservation, and reconstruction completeness while
maintaining high efficiency. Code is available at
https://github.com/Fictionarry/GeoSVR.