用可扩展的4D高斯点云重建动态场景，兼顾几何精度与运动一致性
MotionScale: Reconstructing Appearance, Geometry, and Motion of Dynamic Scenes with Scalable 4D Gaussian Splatting
- 基于聚类中心的运动场，自适应捕捉复杂动态变化
- 长时序下仍保持高保真结构与运动连续性，优于现有方法
- 适合需要长期稳定动态场景重建的研究与应用
从单目视频中真实还原动态4D场景对理解物理世界至关重要。尽管神经渲染取得进展，现有方法在复杂环境中的3D几何恢复和时间一致性运动建模仍面临挑战。为此，我们提出MotionScale，一种可扩展的4D高斯点云框架，能在大场景和长序列下高效运行，同时保持高保真结构与运动一致性。核心是基于聚类中心的运动场参数化，可自适应扩展以捕捉多样且演化的运动模式。为确保长时间鲁棒重建，引入两阶段解耦优化策略：1）背景扩展阶段，适应新可见区域，精修相机位姿，并显式建模瞬时阴影；2）前景传播阶段，通过三阶段精细化过程强制运动一致性。在多个真实世界基准上的大量实验表明，MotionScale在重建质量与时间稳定性上显著优于当前最优方法。
原文摘要 · Abstract (English)
Realistic reconstruction of dynamic 4D scenes from monocular videos is essential for understanding the physical world. Despite recent progress in neural rendering, existing methods often struggle to recover accurate 3D geometry and temporally consistent motion in complex environments. To address these challenges, we propose MotionScale, a 4D Gaussian Splatting framework that scales efficiently to large scenes and extended sequences while maintaining high-fidelity structural and motion coherence. At the core of our approach is a scalable motion field parameterized by cluster-centric basis transformations that adaptively expand to capture diverse and evolving motion patterns. To ensure robust reconstruction over long durations, we introduce a progressive optimization strategy comprising two decoupled propagation stages: 1) A background extension stage that adapts to newly visible regions, refines camera poses, and explicitly models transient shadows; 2) A foreground propagation stage that enforces motion consistency through a specialized three-stage refinement process. Extensive experiments on challenging real-world benchmarks demonstrate that MotionScale significantly outperforms state-of-the-art methods in both reconstruction quality and temporal stability. Project page: https://hrzhou2.github.io/motion-scale-web/.
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