发表机构
Macau University of Science and Technology（澳门科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出事件驱动马尔可夫链高斯泼溅（EdMCGS），利用事件流补充极低帧率RGB帧间缺失信息，直接由事件驱动3D高斯运动，实现高质量动态场景重建与实时渲染。
AI 中文摘要
我们提出了EdMCGS（事件驱动马尔可夫链高斯泼溅），一种端到端方法，用于从极低帧率RGB图像和事件流重建动态3D场景，并可在任意中间时间戳进行渲染。仅依赖RGB图像的方法由于缺乏连续帧之间的证据而产生大量伪影。为了补充缺失的证据，我们将场景运动建模为事件驱动的马尔可夫链，其中稀疏的RGB帧在其各自时间戳锚定状态，而区间内记录的事件驱动跨区间的状态转移。由于转移过程读取当前区间的事件，它在推理时保持活跃，并直接从事件而非通过插值生成3D高斯之间的中间运动，这使我们的方法区别于先前仅将事件用作训练时监督的工作。状态由一组紧凑的控制点承载，每个控制点由其自身图像投影邻域内采样的事件驱动，并且时间局部等距项保持传播运动的局部刚性。在合成和真实场景上的实验表明，EdMCGS在性能上优于基于RGB和基于事件的基线方法，同时以远少于最强事件基线的3D高斯数量实现实时渲染。我们在https URL发布了源代码和一个新数据集。
英文摘要
We present EdMCGS (Event-driven Markov chain Gaussian Splatting), an end-to-end method for reconstructing dynamic 3D scenes from extreme-low-frame-rate RGB together with an event stream, which can then be rendered at any intermediate timestamp. Methods relying solely on RGB images generate numerous artifacts due to the lack of evidence from between consecutive frames. To supply this missing evidence, we model the scene motion as an event-driven Markov chain, in which the sparse RGB frames anchor the state at their own timestamps while the events recorded within an interval drive the transition across it. Since the transition reads the events of the current interval, it remains active at inference and produces the in-between motion of the 3D Gaussians directly from the events rather than by interpolation, which sets our method apart from prior work that uses events only as training-time supervision. The state is carried by a compact set of control points, each driven by the events sampled in the neighborhood of its own image projection, and a temporal local isometry term keeps the propagated motion locally rigid. Experiments on synthetic and real-world scenes show that EdMCGS outperforms both RGB-based and event-based baselines, while rendering in real time with far fewer Gaussians than the strongest event-based baseline. We release our source code and a new dataset at https://github.com/joseclipse/EdMCGS.
Comments29 pages, 3 figures
