基于偏振不透明度先验的快速紧凑三维高斯溅射

中文摘要 AI 辅助

三维高斯溅射（3DGS）在实时速度下达到了最先进的渲染质量，但存在“模型膨胀”问题——大量冗余、低不透明度的高斯会增加内存使用和训练成本。这种低效源于标准的“先稠密化后剪枝”范式，该范式在依赖剪枝实现紧凑性之前会激进地扩展模型。为缓解该问题，我们提出了一种高效的训练框架，构建本质上紧凑的表示，替代传统的先稠密化后剪枝循环。我们的方法利用协同设计：L2重建损失提供与误差成比例的梯度以稳定优化，以及新颖的偏振不透明度先验（POP）主动管理高斯种群。POP引导信息原基向全不透明度发展，非信息原基向透明发展，实现自然剪枝并通过早期光线终止加速渲染。在三个公开数据集上的实验表明，我们的方法在保持相当视觉重建质量的同时，以显著更少的高斯实现了加速的3DGS训练。这些结果表明，所提出的框架为快速且本质紧凑的3DGS训练提供了简单有效的路径。

英文摘要

3D Gaussian Splatting (3DGS) achieves state-of-the-art rendering quality at real-time speeds but suffers from "model bloat" - a large number of redundant, low-opacity Gaussians that inflate memory usage and training costs. This inefficiency stems from the standard "densify-then-prune" paradigm, which expands the model aggressively before relying on pruning to achieve compactness. To mitigate this problem, we present an efficient training framework that builds an intrinsically compact representation, replacing the conventional densify-then-prune cycle. Our method leverages a synergistic design: an L2 reconstruction loss to provide error-proportional gradients that stabilize optimization, and a novel Polarized Opacity Prior (POP) to actively manage the Gaussian population. POP steers informative primitives toward full opacity and uninformative ones toward transparency, enabling natural pruning and accelerating rendering through Early Ray Termination. Experiments on three public datasets demonstrate that our approach consistently achieves accelerated 3DGS training with significantly fewer Gaussians while maintaining comparable visual reconstruction quality. These results show that the proposed framework provides a simple and effective path toward fast and inherently compact 3DGS training.

发表机构

• National Tsing Hua University（国立清华大学）
• National Institute of Informatics（情报通信研究机构）

机构由 AI 辅助整理，请以论文原文为准。