发表机构
Chung-Ang University（中央大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对3DGS存储开销大且现有掩码剪枝方法因Gumbel-Sigmoid导致掩码分布不稳定、排序不可靠的问题，提出LinearMask-GS，用线性增量激活函数获得稳定单峰分布，在Mip-NeRF 360上实现显著高斯缩减并提升渲染质量。
AI 中文摘要
3D高斯泼溅（3DGS）能够实现实时新视角合成，但通过自适应致密化会产生数百万个图元，导致显著的存储开销。诸如LP-3DGS之类的学习掩码剪枝方法通过为每个高斯分配一个可学习的掩码来识别并剪除冗余图元。然而，我们发现了该范式的一个局限性：在掩码训练窗口较短且重要性排序尚未稳定之前，Gumbel-Sigmoid激活函数的陡峭斜率会将掩码值推向极端，产生尖锐的双峰分布，使得排序无法再被可靠地恢复。我们提出了LinearMask-GS，用线性增量激活函数替代Gumbel-Sigmoid，使掩码值在整个掩码训练期间保持在中置信区间，产生稳定的单峰掩码分布，其排序能够追踪重要性。在Mip-NeRF 360上，我们的方法相较于3DGS和LP-3DGS分别实现了3.6倍和1.6倍的高斯数量缩减，同时保持或提升了渲染质量。对于户外场景，它实现了1.6倍的缩减（从218万降至136万），并在PSNR（+0.38 dB）、SSIM（+0.025）和LPIPS（-0.029）上取得了显著提升。
英文摘要
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).
CommentsAccepted to BMVC 2026. 17 pages main paper + 17 pages supplementary material, 3 figures, 4 tables in the main paper
