Adaptive densification is the engine of 3D Gaussian Splatting (3DGS). However, when transposed to the optimization-based Generative Distillation paradigm, this reconstruction-native mechanism reveals fundamental limitations, resulting in inefficient representations cluttered with redundant primitives. We diagnose this failure as a Densification Dilemma stemming from the stochastic nature of generative guidance: the standard magnitude-based accumulation indiscriminately aggregates transient noise alongside geometric signals, making it difficult to strike a balance between over-densification and under-fitting. To resolve this, we introduce Context-Adaptive Moment Estimation (CAdam), a novel framework that reinterprets densification as a statistically grounded signal verification problem. CAdam leverages the first moment of gradients to exploit the interference principle, where stochastic fluctuations cancel out via destructive interference while consistent geometric drifts accumulate via constructive interference, effectively disentangling the underlying signal from the generative noise floor. This is further augmented by a quantile-based context awareness and an intrinsic Signal-to-Noise Ratio (SNR) gating mechanism, which ensure robust adaptation across optimization stages and enable the soft termination of densification. Extensive experiments across diverse objectives (SDS, ISM, VFDS) and strong generative 3DGS backbones show that CAdam reduces Gaussian count by 85%-97% relative to standard densification while preserving overall comparable perceptual quality. These results highlight signal-aware density control as a practical way to improve memory efficiency in optimization-based generative distillation.

翻译：自适应稠密化是三维高斯泼溅（3DGS）的核心引擎。然而，当将其迁移至基于优化的生成式蒸馏范式时，这种原生用于重建的机制暴露了根本性缺陷，导致生成冗余图元堆积的低效表示。我们将此失败归因于生成式引导的随机性引发的“稠密化困境”：基于幅度的标准累积方式无差别地聚合瞬态噪声与几何信号，使得在过稠密化与欠拟合之间难以取得平衡。为解决此问题，我们提出上下文自适应矩估计（CAdam）——一种将稠密化重新诠释为基于统计的信号验证问题的新框架。CAdam利用梯度的第一矩来施加干涉原理：随机波动通过相消干涉相互抵消，而一致性几何漂移通过相长干涉逐渐累积，从而有效将潜在信号从生成噪声本底中分离。该机制进一步通过基于分位数的上下文感知与内置信噪比（SNR）门控机制进行增强，确保在不同优化阶段实现稳健的自适应，并支持稠密化的软终止。面向多样化目标（SDS、ISM、VFDS）与强性能生成式3DGS主干网络的广泛实验表明，相比标准稠密化方法，CAdam将高斯图元数量减少85%-97%，同时保持整体可比的感知质量。这些结果凸显了信号感知的密度控制作为提升基于优化的生成式蒸馏中内存效率的实用路径。