通过动态调整渲染分辨率与点数，200秒内加速3D高斯溅射优化
DashGaussian: Optimizing 3D Gaussian Splatting in 200 Seconds
- 分频渐进拟合思路，按频率分阶段优化
- 平均提速45.7%，保持渲染质量不变
- 适合需要快速重建的实时3D应用
3D高斯溅射（3DGS）通过渲染高斯原语生成像素，其优化时间主要由渲染分辨率和原语数量决定。本文提出DashGaussian，一种针对3DGS优化复杂度的调度方案，通过去除冗余复杂度实现加速。具体而言，将3DGS优化建模为逐步拟合训练视图中更高频率成分的过程，并设计动态渲染分辨率机制以大幅降低优化复杂度。此外，我们指出特定渲染分辨率应与合适的原语数量协同，以平衡计算冗余与拟合质量，因此同步调度原语增长与分辨率提升。大量实验表明，该方法在多种3DGS骨干模型上平均加速45.7%，同时保持渲染质量。
原文摘要 · Abstract (English)
3D Gaussian Splatting (3DGS) renders pixels by rasterizing Gaussian primitives, where the rendering resolution and the primitive number, concluded as the optimization complexity, dominate the time cost in primitive optimization. In this paper, we propose DashGaussian, a scheduling scheme over the optimization complexity of 3DGS that strips redundant complexity to accelerate 3DGS optimization. Specifically, we formulate 3DGS optimization as progressively fitting 3DGS to higher levels of frequency components in the training views, and propose a dynamic rendering resolution scheme that largely reduces the optimization complexity based on this formulation. Besides, we argue that a specific rendering resolution should cooperate with a proper primitive number for a better balance between computing redundancy and fitting quality, where we schedule the growth of the primitives to synchronize with the rendering resolution. Extensive experiments show that our method accelerates the optimization of various 3DGS backbones by 45.7% on average while preserving the rendering quality.
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