Radiance fields represented by 3D Gaussians excel at synthesizing novel views, offering both high training efficiency and fast rendering. However, with sparse input views, the lack of multi-view consistency constraints results in poorly initialized point clouds and unreliable heuristics for optimization and densification, leading to suboptimal performance. Existing methods often incorporate depth priors from dense estimation networks but overlook the inherent multi-view consistency in input images. Additionally, they rely on multi-view stereo (MVS)-based initialization, which limits the efficiency of scene representation. To overcome these challenges, we propose a view synthesis framework based on 3D Gaussian Splatting, named MCGS, enabling photorealistic scene reconstruction from sparse input views. The key innovations of MCGS in enhancing multi-view consistency are as follows: i) We introduce an initialization method by leveraging a sparse matcher combined with a random filling strategy, yielding a compact yet sufficient set of initial points. This approach enhances the initial geometry prior, promoting efficient scene representation. ii) We develop a multi-view consistency-guided progressive pruning strategy to refine the Gaussian field by strengthening consistency and eliminating low-contribution Gaussians. These modular, plug-and-play strategies enhance robustness to sparse input views, accelerate rendering, and reduce memory consumption, making MCGS a practical and efficient framework for 3D Gaussian Splatting.
以3D高斯表示的辐射场在新视角合成中表现出色，既具备高效的训练能力，又能实现快速渲染。然而，在稀疏输入视角的情况下，缺乏多视角一致性约束，导致点云初始化较差，优化和密集化过程中依赖的不可靠启发式方法，进而导致性能不佳。现有方法通常借助于密集估计网络中的深度先验，但忽视了输入图像中固有的多视角一致性。此外，它们依赖于基于多视角立体（MVS）的初始化，这限制了场景表示的效率。为了解决这些问题，我们提出了基于3D高斯散射的视角合成框架——MCGS，能够从稀疏输入视角进行照片级真实感的场景重建。MCGS在增强多视角一致性方面的关键创新如下： i) 我们引入了一种初始化方法，结合稀疏匹配器和随机填充策略，生成紧凑但足够的初始点集。这种方法增强了几何先验，促进了高效的场景表示。 ii) 我们开发了一种基于多视角一致性的渐进修剪策略，通过加强一致性并消除低贡献的高斯点来优化高斯场。这些模块化的即插即用策略提升了稀疏输入视角下的鲁棒性，加速了渲染并减少了内存消耗，使MCGS成为一个实用且高效的3D高斯散射框架。