Multi-period image collections are common in real-world applications. Cities are re-scanned for mapping, construction sites are revisited for progress tracking, and natural regions are monitored for environmental change. Such data form multi-period scenes, where geometry and appearance evolve. Reconstructing such scenes is an important yet underexplored problem. Existing pipelines rely on incompatible assumptions: static and in-the-wild methods enforce a single geometry, while dynamic ones assume smooth motion, both failing under long-term, discontinuous changes. To solve this problem, we introduce ChronoGS, a temporally modulated Gaussian representation that reconstructs all periods within a unified anchor scaffold. It's also designed to disentangle stable and evolving components, achieving temporally consistent reconstruction of multi-period scenes. To catalyze relevant research, we release ChronoScene dataset, a benchmark of real and synthetic multi-period scenes, capturing geometric and appearance variation. Experiments demonstrate that ChronoGS consistently outperforms baselines in reconstruction quality and temporal consistency. Our code and the ChronoScene dataset are publicly available at https://github.com/ZhongtaoWang/ChronoGS.

翻译：多时段图像集合在现实应用中普遍存在。城市因地图测绘被重复扫描，建筑工地因进度跟踪被定期回访，自然区域因环境监测被持续观测。此类数据构成多时段场景，其几何形态与外观随时间演化。重建此类场景是重要但尚未充分探索的问题。现有管线依赖互不兼容的假设：静态与野外方法强制采用单一几何结构，而动态方法假设平滑运动，两者均无法应对长期、非连续的变化。为解决此问题，我们提出ChronoGS——一种时序调制的高斯表示，可在统一锚定支架内重建所有时段。其设计还旨在解耦稳定成分与演化成分，实现对多时段场景的时序一致性重建。为催化相关研究，我们发布ChronoScene数据集，这是一个包含真实与合成多时段场景的基准测试集，捕捉了几何与外观变化。实验表明，ChronoGS在重建质量与时间一致性上持续优于基线方法。我们的代码与ChronoScene数据集公开于https://github.com/ZhongtaoWang/ChronoGS。