CoMapGS：基于共视性地图的高斯溅射用于稀疏新视角合成
CoMapGS: Covisibility Map-based Gaussian Splatting for Sparse Novel View Synthesis
AI总结：
CoMapGS提出基于共视性地图的高斯溅射方法，通过构建共视性地图、增强点云和不确定性加权监督，解决稀疏新视角合成中区域不确定性问题，在Mip-NeRF 360和LLFF数据集上超越现有方法。
摘要：
我们提出了基于共视性地图的高斯溅射（CoMapGS），旨在恢复稀疏新视角合成中未被充分表示的稀疏区域。CoMapGS通过构建共视性地图、增强初始点云以及使用基于邻近分类器的不确定性加权监督，同时处理高不确定性和低不确定性区域。我们的贡献有三方面：（1）CoMapGS通过利用共视性地图作为核心组件来重新定义新视角合成，以解决区域特定的不确定性；（2）为低和高不确定性区域增强的初始点云补偿了由COLMAP派生的稀疏点云，提高了重建质量，并有益于少样本3DGS方法；（3）基于共视性分数加权和邻近分类的自适应监督，在具有不同稀疏度分数的场景中实现了一致的性能提升，这些分数源自共视性地图。实验结果表明，CoMapGS在包括Mip-NeRF 360和LLFF在内的数据集上优于最先进的方法。
英文摘要：
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.
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