ChronoFuseGS：多时相高斯融合与逐样点持久性及变化可视化
ChronoFuseGS: Multi-Temporal Gaussian Fusion with Per-Splat Persistence and Change Visualization
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中文总结 AI 辅助
ChronoFuseGS通过融合多个时间步的高斯泼溅模型，实现场景持久部分细化与增量扩展，并提出变化感知可视化，在真实洪水区域数据集上优于单时间步模型。
中文摘要 AI 辅助
重建场景中部分内容在不同采集图像集之间发生变化的场景，对三维场景重建构成了挑战。我们提出ChronoFuseGS，一种多时相高斯泼溅方法，通过将多个分别训练的高斯泼溅模型（每个模型代表一个不同的时间步，且地理覆盖范围部分重叠）合并为一个组合模型来解决这一问题。通过允许一个时间步的高斯为其他时间步的重建做出贡献，我们的方法利用所有采集时间步的数据来细化场景中的持久部分。该模型支持增量扩展，允许在保留现有合并重建结果的同时添加新的时间步。它为每个高斯基元编码其贡献重建的时间步。为支持重建场景的视觉探索，我们提出了一种变化感知的可视化方法，该方法在保留持久部分颜色的同时，突出显示用户定义时间选择内场景中发生变化的部分。由于持久性编码在高斯基元级别操作，变化以子对象粒度可视化，而非仅限于对象级变化。我们在一个真实世界的户外洪水管理区域数据集上评估了我们的方法，该数据集在7个月内跨越8个记录日采集，涵盖季节性植被变化、积雪覆盖和洪水事件，我们将其公开。我们的结果表明，组合模型在新视角合成质量上始终优于单独训练的单个时间步模型，恢复了单独重建中缺失的结构细节，并可靠地突出显示精细细节以及对象和自然结构子部分的变化。
英文摘要
Reconstructing environments where parts of the scene change between captured image sets poses a challenge for 3D scene reconstruction. We present ChronoFuseGS, a multi-temporal Gaussian Splatting approach that addresses this issue by taking multiple separately trained Gaussian Splatting models, each representing a distinct timestep and partially overlapping in geographic coverage, and merging them into a single combined model. By allowing Gaussians from one timestep to contribute to the reconstruction at other timesteps, our approach leverages data across all captured timesteps to refine persistent parts of the scene. The model supports incremental extension, allowing new timesteps to be added while preserving the existing merged reconstruction. It encodes, for each Gaussian primitive, at which timesteps it contributes to the reconstruction. To support visual exploration of the reconstructed scene, we present a change-aware visualization approach that highlights the parts of the scene that have changed across a user-defined time selection, while preserving the color of persistent parts. Since the persistence encoding operates at the Gaussian primitive level, changes are visualized at sub-object granularity rather than being limited to object-level changes. We evaluate our approach on a real-world outdoor dataset of a flood management area, captured over 7 months across eight recording days and covering seasonal vegetation changes, snow cover, and flooding events, which we make publicly available. Our results demonstrate that the combined model consistently outperforms individually trained single-timestep models in novel-view synthesis quality, recovers structural details absent in the individual reconstructions, and reliably highlights changes in fine details and sub-parts of objects and natural structures.
