MoRGS：面向可流式动态3D场景的高效逐高斯运动推理
MoRGS: Efficient Per-Gaussian Motion Reasoning for Streamable Dynamic 3D Scenes
将稀疏关键视图的光流作为轻量运动线索，引导每个高斯球的运动朝向真实场景动态，并学习运动偏移场和置信度来补偿光流的稀疏性与视角依赖性，为在线动态重建提供可复用的运动推理模块。
原文依据 efficiently leverage optical flow on a sparse set of key views as a lightweight motion cue to guide per-Gaussian motion
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论动态场景的在线重建旨在低延迟约束下从流式多视图输入中学习。3D高斯泼溅的快速训练和实时渲染能力使得即时重建在实际中可行，从而实现了在线4D重建。然而，现有的在线方法尽管高效且视觉质量良好，却无法学习反映真实场景动态的逐高斯运动。在没有显式运动线索的情况下，外观和运动仅通过光度损失进行优化，导致逐高斯运动追逐像素残差而非真实3D运动。为了解决这个问题，我们提出了MoRGS，一个高效的在线逐高斯运动推理框架，将高斯运动作为核心建模对象。具体来说，我们高效地利用稀疏关键视图上的光流作为轻量级运动线索，引导逐高斯运动朝向场景的真实动态。为了补偿光流的稀疏性和视角依赖性，我们学习了一个逐高斯运动偏移场，以协调投影的3D运动与跨视图和跨时间观察到的光流之间的差异。此外，我们引入了一种逐高斯运动置信度，用于区分动态和静态高斯，并对高斯属性残差更新进行加权，从而抑制静态区域中的冗余运动，以获得更好的时序一致性并加速大运动的建模。大量实验表明，MoRGS在在线方法中实现了最优的重建质量和运动保真度，同时保持了可流式性能。
英文原文摘要 1611 字符
Online reconstruction of dynamic scenes aims to learn from streaming multi-view inputs under low-latency constraints. The fast training and real-time rendering capabilities of 3D Gaussian Splatting have made on-the-fly reconstruction practically feasible, enabling online 4D reconstruction. However, existing online approaches, despite their efficiency and visual quality, fail to learn per-Gaussian motion that reflects true scene dynamics. Without explicit motion cues, appearance and motion are optimized solely under photometric loss, causing per-Gaussian motion to chase pixel residuals rather than true 3D motion. To address this, we propose MoRGS, an efficient online per-Gaussian motion reasoning framework that treats Gaussian movement as a core modeling object. Specifically, we efficiently leverage optical flow on a sparse set of key views as a lightweight motion cue to guide per-Gaussian motion toward the scene's true dynamics. To compensate for the sparsity and view-dependence of flow, we learn a per-Gaussian motion offset field that reconciles discrepancies between projected 3D motion and observed flow across views and time. In addition, we introduce a per-Gaussian motion confidence that separates dynamic from static Gaussians and weights Gaussian attribute residual updates, thereby suppressing redundant motion in static regions for better temporal consistency and accelerating the modeling of large motions. Extensive experiments demonstrate that MoRGS achieves state-of-the-art reconstruction quality and motion fidelity among online methods, while maintaining streamable performance.