Complexity Aware Continuous Level of Details for Gaussian Splatting

3DGS enables real-time novel view synthesis, but practical deployment under varying compute budgets requires a single Gaussian set that remains effective when truncated to a prefix of primitives. This raises how capacity should be allocated as the budget decreases.We observe two findings. First, image complexity correlates with reconstruction error in both image and 3D Gaussian space. Second, this structure degrades under strong budget reduction, indicating suboptimal allocation.These observations motivate using image complexity as an optimization signal. We propose a continuous level-of-detail method based on 3DGS-MCMC that uses it for (i) relocation toward difficult regions during training and (ii) importance-based retention of simple and detailed regions at inference. A variance constraint on residuals between full- and reduced-budget renderings further enforces uniform error under compression. Our method yields smoother LOD degradation than prior work, with largest gains in low-budget regimes. Code will be released.