While 3D Gaussian Splatting (3DGS) has demonstrated remarkable performance in novel view synthesis and real-time rendering, the high memory consumption due to the use of millions of Gaussians limits its practicality. To mitigate this issue, improvements have been made by pruning unnecessary Gaussians, either through a hand-crafted criterion or by using learned masks. However, these methods deterministically remove Gaussians based on a snapshot of the pruning moment, leading to sub-optimized reconstruction performance from a long-term perspective. To address this issue, we introduce MaskGaussian, which models Gaussians as probabilistic entities rather than permanently removing them, and utilize them according to their probability of existence. To achieve this, we propose a masked-rasterization technique that enables unused yet probabilistically existing Gaussians to receive gradients, allowing for dynamic assessment of their contribution to the evolving scene and adjustment of their probability of existence. Hence, the importance of Gaussians iteratively changes and the pruned Gaussians are selected diversely. Extensive experiments demonstrate the superiority of the proposed method in achieving better rendering quality with fewer Gaussians than previous pruning methods, pruning over 60% of Gaussians on average with only a 0.02 PSNR decline.
尽管三维高斯散射（3D Gaussian Splatting, 3DGS）在新视角合成和实时渲染方面表现出色，但由于使用了数百万个高斯点，其高内存消耗限制了实际应用的可行性。为缓解这一问题，一些改进方法通过手工设计的标准或学习生成的掩码来修剪不必要的高斯点。然而，这些方法在修剪时基于某一时刻的快照确定性地移除高斯点，从长远来看可能导致次优的重建性能。 为了解决这一问题，我们提出了MaskGaussian，将高斯点建模为概率性实体，而非永久移除，并根据其存在的概率来利用它们。为实现这一目标，我们设计了一种掩码光栅化技术（masked-rasterization technique），使得那些未被使用但概率上仍存在的高斯点能够接收梯度，从而动态评估它们对场景演化的贡献，并调整其存在的概率。因此，高斯点的重要性能够迭代地发生变化，修剪过程中的选择也更加多样化。 大量实验表明，与以往的修剪方法相比，MaskGaussian在使用更少高斯点的情况下实现了更好的渲染质量。平均而言，该方法能够修剪超过60%的高斯点，仅带来0.02 PSNR的微小下降。