Recently, 3D Gaussian Splatting (3D-GS) has prevailed in novel view synthesis, achieving high fidelity and efficiency. However, it often struggles to capture rich details and complete geometry. Our analysis highlights a key limitation of 3D-GS caused by the fixed threshold in densification, which balances geometry coverage against detail recovery as the threshold varies. To address this, we introduce a novel densification method, residual split, which adds a downscaled Gaussian as a residual. Our approach is capable of adaptively retrieving details and complementing missing geometry while enabling progressive refinement. To further support this method, we propose a pipeline named ResGS. Specifically, we integrate a Gaussian image pyramid for progressive supervision and implement a selection scheme that prioritizes the densification of coarse Gaussians over time. Extensive experiments demonstrate that our method achieves SOTA rendering quality. Consistent performance improvements can be achieved by applying our residual split on various 3D-GS variants, underscoring its versatility and potential for broader application in 3D-GS-based applications.
近年来，3D高斯投影（3D Gaussian Splatting, 3D-GS）在新视角合成任务中表现突出，兼具高保真度和高效率。然而，其在捕捉丰富细节和完整几何结构方面常显不足。我们的分析表明，3D-GS 的主要限制在于固定的稠密化阈值。该阈值的变化在几何覆盖和细节恢复之间形成权衡，从而影响整体表现。 为解决这一问题，我们提出了一种新颖的稠密化方法——残差分裂（Residual Split）。该方法通过添加一个缩小尺度的高斯作为残差，能够自适应地恢复细节并补充缺失的几何，同时支持渐进式优化。此外，为配合该方法，我们设计了一条名为 ResGS 的流水线。具体而言，我们集成了高斯图像金字塔，用于渐进式监督，并实现了一种选择机制，优先对粗略高斯进行稠密化处理。 广泛的实验表明，我们的方法在渲染质量上达到了当前最先进（SOTA）的水平。通过在多种 3D-GS 变体中应用残差分裂，均可实现一致的性能提升，这突显了其广泛适用性及在 3D-GS 应用中的潜力。