GeoSVR: Taming Sparse Voxels for Geometrically Accurate Surface Reconstruction

2025-09-22

AI Summary

Addressing the challenge of simultaneously achieving high geometric accuracy, completeness, and fine-detail fidelity in sparse-voxel surface reconstruction, this paper proposes GeoSVR—an explicit volumetric framework. Our method integrates differentiable rendering with explicit voxel optimization while introducing two key innovations: (1) uncertainty-aware depth supervision, which leverages per-pixel uncertainty maps from monocular depth estimation to stabilize optimization and improve convergence robustness; and (2) sparse-voxel surface regularization, enforcing geometric consistency to enhance surface sharpness and topological integrity—especially for small-scale voxels. By jointly optimizing voxel occupancy and surface geometry under these constraints, GeoSVR achieves efficient computation without sacrificing reconstruction quality. Extensive experiments demonstrate that GeoSVR outperforms state-of-the-art radiance fields and both implicit and explicit reconstruction methods across diverse complex scenes, delivering superior geometric accuracy, more complete surface coverage, and better preservation of fine geometric details.

Technology Category

• Computer Vision: 3D Computer Vision
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Semantics and Knowledge: Representation, semantic annotation, enhancement, enrichments, access and/or integration of a variety of data on the Web

Abstract

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.

Problem

• Overcoming representational bottlenecks in surface reconstruction methods
• Addressing sparse voxels' challenges in geometric accuracy and completeness
• Enhancing geometric consistency for sharp and accurate surface formation

Innovation

• Voxel-Uncertainty Depth Constraint for robust scene constraints
• Sparse Voxel Surface Regularization enhances geometric consistency
• Explicit voxel-based framework using sparse voxels for reconstruction