Abstract
We present MOSAIC-GS, a novel, fully explicit, and computationally efficient approach for high-fidelity dynamic scene reconstruction from monocular videos using Gaussian Splatting. Monocular reconstruction is inherently ill-posed due to the lack of sufficient multiview constraints, making accurate recovery of object geometry and temporal coherence particularly challenging. To address this, we leverage multiple geometric cues, such as depth, optical flow, dynamic object segmentation, and point tracking. Combined with rigidity-based motion constraints, these cues allow us to estimate preliminary 3D scene dynamics during an initialization stage. Recovering scene dynamics prior to the photometric optimization reduces reliance on motion inference from visual appearance alone, which is often ambiguous in monocular settings. To enable compact representations, fast training, and real-time rendering while supporting non-rigid deformations, the scene is decomposed into static and dynamic components. Each Gaussian in the dynamic part of the scene is assigned a trajectory represented as time-dependent Poly-Fourier curve for parameter-efficient motion encoding. We demonstrate that MOSAIC-GS achieves substantially faster optimization and rendering compared to existing methods, while maintaining reconstruction quality on par with state-of-the-art approaches across standard monocular dynamic scene benchmarks.
Abstract (translated)
我们介绍了MOSAIC-GS，这是一种新颖的、完全显式的计算效率高的方法，用于从单目视频中进行高保真动态场景重建。这种方法利用了高斯点积（Gaussian Splatting）技术。由于缺乏足够的多视角约束条件，单目重建本质上是一个病态问题，这使得准确恢复物体几何形状和时间一致性变得特别具有挑战性。为了解决这一问题，我们采用了多种几何线索，例如深度信息、光学流、动态对象分割以及点跟踪。结合基于刚体的运动限制，这些线索使我们在初始化阶段能够估计出初步的3D场景动力学。 在光度优化之前恢复场景的动力学减少了对仅从视觉外观推断运动的依赖，在单目设置中这种推断往往是模棱两可的。为了实现紧凑表示、快速训练和实时渲染，并支持非刚性变形，场景被分解为静态和动态部分。每个动态部分中的高斯点都分配了一个由时间相关的Poly-Fourier曲线表示的轨迹，以进行参数高效的运动编码。 我们证明了MOSAIC-GS在与现有方法相比时，在标准单目动态场景基准测试中实现了显著更快的优化和渲染速度，同时保持了与最先进的重建质量相当的水平。
URL
https://arxiv.org/abs/2601.05368
