ChronoGS：解耦多期场景中的不变量与变化
ChronoGS: Disentangling Invariants and Changes in Multi-Period Scenes
把多期采集的图像集合统一到一套锚点支架上的时序调制高斯表示，并显式解耦稳定几何与变化成分，为长期、非连续变化场景（城市重扫描、施工进度、环境监测）的新视角合成提供了可复用的表示与评测基准。
原文依据 a temporally modulated Gaussian representation that reconstructs all periods within a unified anchor scaffold
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论多期图像集合在现实应用中十分常见。城市会被重新扫描以用于测绘，施工场地会被反复回访以跟踪进度，自然区域会被持续监测以观察环境变化。这类数据构成多期场景，其中的几何与外观会随时间演化。重建此类场景是一个重要但尚未被充分探索的问题。现有流程依赖互不兼容的假设：静态方法与野外（in-the-wild）方法强制单一几何，而动态方法假设运动是平滑的，二者在长期、非连续的变化下都会失效。为解决该问题，我们提出 ChronoGS，一种时序调制的高斯表示，它在统一的锚点支架内重建所有时期。该表示还被设计用于解耦稳定成分与演化成分，从而实现多期场景的时序一致重建。为推动相关研究，我们发布 ChronoScene 数据集，这是一个包含真实与合成多期场景的基准，涵盖几何与外观的变化。实验表明，ChronoGS 在重建质量与时序一致性上均持续优于基线方法。我们的代码与 ChronoScene 数据集将公开。
英文原文摘要 1222 字符
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 will be made publicly available.