高斯點降取樣的修訂

Revising Densification in Gaussian Splatting

April 9, 2024

作者: Samuel Rota Bulò, Lorenzo Porzi, Peter Kontschieder

cs.AI

摘要

本文討論了在三維高斯噴灑（3DGS）中自適應密度控制（ADC）的限制，這是一種實現高質量、照片逼真結果的場景表示方法，用於新視角合成。ADC已被引入自動三維點基元管理，控制稠密化和修剪，但在稠密化邏輯上存在一定限制。我們的主要貢獻是在3DGS中提出一種更有原則性的、以像素誤差驅動的密度控制公式，利用輔助的、以每像素誤差函數作為稠密化標準。我們進一步引入一種機制來控制每個場景生成的基元總數，並在克隆操作期間修正ADC當前不透明度處理策略中的偏差。我們的方法在各種基準場景中帶來了一致的質量改進，而不會犧牲該方法的效率。

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.