SynGS: Synergistic Continual Learning and Change Detection with Gaussian Splatting

3D Gaussian Splatting enables high-fidelity, real-time novel view synthesis, yet adapting it to dynamic, evolving scenes remains challenging. Existing methods rely on fixed static-dynamic partitioning, which suffers from persistent partitioning errors and cannot adapt during optimization. To tackle this issue, we introduce SynGS, a unified 3D Gaussian framework for continual scene updating and multi-view change detection. We cast static-dynamic decomposition as a learnable task, jointly optimizing per-Gaussian change probabilities and scene representations for iterative refinement. We further propose a change-guided update strategy that modulates illumination-aware photometric corrections via learned probabilities, separating transient appearance variations from authentic scene changes. Projecting Gaussian-level probabilities to the image plane yields 3D-consistent change masks, enabling mutual optimization between reconstruction and change localization. An illumination-decoupled refinement step further improves mask quality. Evaluations on standard benchmarks show that SynGS outperforms baseline methods on scene update and change detection tasks, while fully retaining the real-time rendering capability of 3D Gaussian Splatting. Code will be released upon acceptance.