NeurIPS 2025 Spotlight | GeoSVR：稀疏体素的新潜力——超越3DGS系列的高精度三维表面重建

Core Viewpoint - The article discusses the introduction of GeoSVR (Geometric Sparse Voxel Reconstruction), a new explicit geometric optimization framework that surpasses existing methods in geometric accuracy, detail capture, and completeness in surface reconstruction from multi-view images [2][32]. Methodology - The core of GeoSVR involves two main designs for harnessing sparse voxels: 1. Voxel-Uncertainty Depth Constraint, which models uncertainty and weights depth constraints to improve geometric accuracy [8][10]. 2. Sparse Voxel Surface Regularization, which employs various regularization strategies to maintain global consistency and prevent overfitting [14][22]. Experimental Results - GeoSVR significantly outperforms existing methods across multiple datasets, achieving a Chamfer distance that is notably better than state-of-the-art methods, with a training time of only 0.8 hours compared to over 12 hours for previous methods [24][30]. - In the DTU dataset, GeoSVR achieved a mean Chamfer distance of 0.32, demonstrating superior geometric precision and reconstruction quality [23][30]. - On the Mip-NeRF 360 dataset, GeoSVR achieved an F1-score of 0.56, marking it as the highest precision method currently available [27]. Significance and Future Outlook - GeoSVR showcases the potential of sparse voxels for high-quality surface reconstruction, providing a foundation for applications in robotics perception, autonomous driving, digital twins, and virtual reality [32][33]. - Future research will focus on scaling scene reconstruction and supporting complex light path conditions [33].