通过残差分裂提升3D高斯点云细节恢复能力
ResGS: Residual Densification of 3D Gaussian for Efficient Detail Recovery
- 提出残差分裂方法，用缩放后的高斯补充细节
- 在多个3D-GS变体上实现最优渲染质量
- 适合需要精细重建的三维视觉应用
最近，3D高斯溅射（3D-GS）在新视角合成中广泛应用，实现了高保真度与高效性。然而，其通常难以捕捉丰富细节和完整几何结构。分析表明，3D-GS的稠密化操作缺乏自适应性，在几何覆盖与细节恢复间存在矛盾。为此，我们提出一种新型稠密化操作——残差分裂，即添加一个缩放后的高斯作为残差。该方法能自适应地恢复细节并补全缺失几何。为进一步支持该方法，我们设计了名为ResGS的流程：引入高斯图像金字塔进行渐进监督，并实现优先对粗粒度高斯进行稠密化的选择机制。大量实验表明，本方法在渲染质量上达到当前最优。将残差分裂应用于多种3D-GS变体均能持续提升性能，验证其通用性与在3D-GS相关应用中的潜力。
原文摘要 · Abstract (English)
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 reveals that the 3D-GS densification operation lacks adaptiveness and faces a dilemma between geometry coverage and detail recovery. To address this, we introduce a novel densification operation, residual split, which adds a downscaled Gaussian as a residual. Our approach is capable of adaptively retrieving details and complementing missing geometry. 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.
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