高斯飞溅中的密集化修订

Revising Densification in Gaussian Splatting

April 9, 2024

作者: Samuel Rota Bulò, Lorenzo Porzi, Peter Kontschieder

cs.AI

摘要

本文讨论了自适应密度控制（ADC）在三维高斯飞溅（3DGS）中的局限性，这是一种实现高质量、逼真效果的新视角合成场景表示方法。ADC被引入用于自动三维点基元管理，控制稠密化和修剪，但在稠密化逻辑方面存在一定限制。我们的主要贡献是在3DGS中为密度控制提出了更加原则性的、以像素误差驱动的公式，利用辅助的、以每像素误差函数作为稠密化标准。我们进一步引入了一种机制来控制每场景生成的基元总数，并在克隆操作期间纠正了ADC当前不透明度处理策略中的偏差。我们的方法在各种基准场景中实现了一致的质量改进，而不牺牲方法的效率。

English

In this paper, we address the limitations of Adaptive Density Control (ADC) in 3D Gaussian Splatting (3DGS), a scene representation method achieving high-quality, photorealistic results for novel view synthesis. ADC has been introduced for automatic 3D point primitive management, controlling densification and pruning, however, with certain limitations in the densification logic. Our main contribution is a more principled, pixel-error driven formulation for density control in 3DGS, leveraging an auxiliary, per-pixel error function as the criterion for densification. We further introduce a mechanism to control the total number of primitives generated per scene and correct a bias in the current opacity handling strategy of ADC during cloning operations. Our approach leads to consistent quality improvements across a variety of benchmark scenes, without sacrificing the method's efficiency.