LinearMask-GS: Stable-Mask Importance Pruning for Compact 3D Gaussian Splatting

2026-09-09

AI Summary

为解决3D高斯点云存储过大问题，提出LinearMask-GS方法，通过线性增长激活函数稳定重要性排序，有效减少冗余点并提高渲染质量。

Abstract

3D Gaussian Splatting (3DGS) enables real-time novel view synthesis but produces millions of primitives through adaptive densification, leading to significant storage overhead. Learned-mask pruning methods such as LP-3DGS address this by assigning each Gaussian a learnable mask to identify and prune redundant primitives. However, we identify a limitation of this paradigm: the steep slope of the Gumbel-Sigmoid activation drives mask values to the extremes within the short mask-training window, before the importance ranking has stabilized, producing a sharply bimodal distribution from which that ranking can no longer be reliably recovered. We propose LinearMask-GS, which replaces Gumbel-Sigmoid with a linear increment activation that keeps mask values in a mid-confidence regime throughout mask training, producing a stable, unimodal mask distribution whose ranking tracks importance. On Mip-NeRF 360, our method achieves 3.6x and 1.6x Gaussian reductions over 3DGS and LP-3DGS, respectively, while maintaining or improving rendering quality. For outdoor scenes, it yields a 1.6x reduction (from 2.18M to 1.36M) with notable gains in PSNR (+0.38 dB), SSIM (+0.025), and LPIPS (-0.029).

Problem

• 3D Gaussian Splatting
• storage overhead
• learned-mask pruning
• Gumbel-Sigmoid activation
• mask values

Innovation

• LinearMask-GS
• linear increment activation
• stable unimodal mask distribution
• importance ranking