EffGS：高效且高保真的高斯泼溅

中文摘要 AI 辅助

三维高斯泼溅（3DGS）实现了实时新视角合成，但现有的通用加速方法在扩展到更复杂、大规模场景时，会遭受严重的渲染质量下降。为解决此问题，我们提出EffGS，一个更通用的加速框架，它在有界和大规模场景中，在保持重建质量与原始3DGS相当或更优的同时，提高训练和渲染效率。EffGS结合了频率感知引导、局部密度控制和自适应基元尺度调制。首先，一种重要性评分机制将逐像素重建误差与随训练调度的差分高斯掩模相结合，以提供分阶段的空间引导。其次，局部致密化和剪枝将密度修改限制在采样视图中具有有效投影足迹的高斯上。第三，可学习的逐高斯尺度调制在优化过程中调整有效基元范围，同时保留紧凑盒光栅化规则。在有界和大规模场景数据集上的大量实验表明，重建质量、训练时间和基元数量之间达到了良好的平衡。组件消融和匹配基元预算的比较进一步支持了该框架的有效性。

英文摘要

3D Gaussian Splatting (3DGS) enables real-time novel view synthesis, but existing general-purpose acceleration methods suffer severe rendering quality degradation when extended to more complex, large-scale scenes. To address this issue, we propose EffGS, a more general acceleration framework that improves training and rendering efficiency while maintaining reconstruction quality comparable to or better than vanilla 3DGS across bounded and large-scale scenes. EffGS combines frequency-aware guidance, localized density control, and adaptive primitive scale modulation. First, an importance scoring mechanism combines pixel-wise reconstruction errors with a difference-of-Gaussians mask scheduled over training to provide stage-dependent spatial guidance. Second, localized densification and pruning restricts density modifications to Gaussians with valid projected footprints in the sampled views. Third, learnable per-Gaussian scale modulation adjusts effective primitive extent during optimization while retaining the Compact Box rasterization rule. Extensive experiments on bounded and large-scale scene datasets demonstrate a favorable balance between reconstruction quality, training time, and primitive count. Component ablations and matched-primitive-budget comparisons further support the effectiveness of the framework.

发表机构

• Beihang University（北京航空航天大学）
• Nanyang Technological University（南洋理工大学）

机构由 AI 辅助整理，请以论文原文为准。