选哪些高斯点影响重建质量？实测发现采样方法影响不大。
Does it matter which Gaussians you pick in 4D Gaussian streaming?
- 用不同方法选8192个高斯锚点，固定其他流程测试效果。
- 4096个随机或均匀采样点效果接近8192个远点采样点。
- 采样非瓶颈，优化策略比选点更重要，适合算法调优者看。
基于锚点的4D高斯流方法（如IGS）每帧从一组紧凑的高斯锚点更新动态场景，通常默认使用远点采样（FPS）在8192个锚点预算下选取。由于这些锚点通过线性混合皮肤驱动整个场景，其选取规则应影响重建质量。本文保持IGS流程不变，仅更换采样器，在N3DV和MeetingRoom数据集上对比了FPS、随机、均匀、基于透明度的启发式方法以及学习策略，在不同预算与优化设置下进行测试。结果表明：在部署预算下，采样器无显著影响——4096个随机或均匀采样点的效果与8192个FPS点在测量误差范围内相当，说明默认8192预算过度配置；该结论在另一骨干模型3DGStream上也成立。学习策略表现参差不齐，仅在紧预算下提升N3DV验证集性能，无法提供跨数据集稳定规则，且选择环节始终非运行时瓶颈。作者将发布完整实验与评估协议，构建采样器基准。
原文摘要 · Abstract (English)
Anchor-driven 4D Gaussian streaming methods such as Instant Gaussian Stream (IGS) update a dynamic scene each frame from a compact set of Gaussian anchors, chosen by default with Farthest Point Sampling (FPS) at a fixed budget of $8{,}192$. Because these anchors act as control points that drive the whole scene through linear blend skinning, the rule used to choose them ought to affect reconstruction quality. We test this by holding the IGS pipeline fixed and changing only the sampler, comparing FPS, random, uniform, an opacity-scale heuristic, and a learned policy across budgets and refinement settings on N3DV and MeetingRoom. At deployment budgets the sampler has no measurable effect: a cheap random or uniform sampler at $4{,}096$ anchors matches FPS@8192 within measurement error, the default budget is over-provisioned, and the result holds on a second backbone (3DGStream). The learned policy is mixed rather than consistently better: it can improve the N3DV validation set at tight budgets, but does not give a stable cross-dataset rule, and selection is never the bottleneck because refinement dominates runtime. We will release our full sweep and evaluation protocol as a sampler benchmark.
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