Fast and Compact 3D Gaussian Splatting with Polarized Opacity Prior

2026-08-23

AI Summary

为解决3D高斯点云渲染中模型臃肿问题，提出了一种结合L2重建损失和极化透明度先验的高效训练框架，以实现更快、更紧凑的3D渲染。

Abstract

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.

Problem

• 3D Gaussian Splatting
• model bloat
• redundant Gaussians
• memory usage
• training costs

Innovation

• Polarized Opacity Prior
• Early Ray Termination
• Intrinsically Compact Representation