Standard Gaussian splatting methods rely on heuristic densification and pruning to adaptively allocate primitives during training, and the resulting Gaussian count strongly influences both reconstruction quality and runtime. This makes comparisons across methods fragile: improvements can stem from higher representational capacity rather than algorithmic design. A common and naive workaround for this is hard-stopping or budgeting densification/pruning once a target count is reached, which biases training because different methods hit the cap at different times, yielding non-uniform densify/prune exposure across views and uneven point distributions. We propose a target point control scheme that preserves the standard densification window and cadence, but adjusts only the existing densification and opacity-culling hyper-parameters to track a quadratic target count trajectory. This quota-governor reaches the desired count by 15k iterations without abrupt cutoffs, ensuring that all methods and views receive equal densification and pruning cycles, enabling fairer, capacity-matched evaluation.

翻译：标准高斯溅射方法在训练过程中依赖启发式稠密化与剪枝策略自适应分配基元，生成的核数量对重建质量与运行时间均有显著影响。这导致不同方法间的对比脆弱：性能提升可能源于更高的表征容量而非算法设计创新。为解决这一问题，常用的朴素手段是在目标计数达到时强制终止或预算控制稠密化/剪枝过程，但不同方法达到阈值的时间差异会导致训练偏差，造成视图间非均匀的稠密化/剪枝暴露与点分布失衡。我们提出一种目标点控制方案，在保持标准稠密化窗口与操作节奏的前提下，仅调整现有稠密化与不透明度剔除超参数以追踪二次目标计数轨迹。该配额调控机制可在15000次迭代内平稳达到目标计数且无突变截断，确保所有方法与视图获得均等的稠密化与剪枝周期，从而实现更公平、容量匹配的评估。