We propose Covisibility Map-based Gaussian Splatting (CoMapGS), designed to recover underrepresented sparse regions in sparse novel view synthesis. CoMapGS addresses both high- and low-uncertainty regions by constructing covisibility maps, enhancing initial point clouds, and applying uncertainty-aware weighted supervision using a proximity classifier. Our contributions are threefold: (1) CoMapGS reframes novel view synthesis by leveraging covisibility maps as a core component to address region-specific uncertainty; (2) Enhanced initial point clouds for both low- and high-uncertainty regions compensate for sparse COLMAP-derived point clouds, improving reconstruction quality and benefiting few-shot 3DGS methods; (3) Adaptive supervision with covisibility-score-based weighting and proximity classification achieves consistent performance gains across scenes with varying sparsity scores derived from covisibility maps. Experimental results demonstrate that CoMapGS outperforms state-of-the-art methods on datasets including Mip-NeRF 360 and LLFF.

翻译：我们提出了基于共视性地图的高斯泼溅方法（CoMapGS），旨在恢复稀疏新视角合成中表征不足的稀疏区域。CoMapGS通过构建共视性地图、增强初始点云以及采用基于邻近分类器的不确定性感知加权监督，同时处理高不确定性和低不确定性区域。我们的贡献主要体现在三个方面：（1）CoMapGS将共视性地图作为核心组件重构新视角合成框架，以解决区域特定的不确定性问题；（2）针对低不确定性和高不确定性区域的增强初始点云弥补了从COLMAP导出的稀疏点云的不足，提升了重建质量，并使少样本3DGS方法受益；（3）基于共视性评分加权的自适应监督与邻近分类相结合，在具有不同共视性地图导出的稀疏度评分的场景中均实现了稳定的性能提升。实验结果表明，在Mip-NeRF 360和LLFF等数据集上，CoMapGS的性能优于现有最先进方法。