用频域分解提升动态场景渲染精度与稳定性
FAST-GS: Frequency Aware Space-time Gaussian Splatting for Photorealistic Dynamic Novel View Synthesis
- 将运动拆解为频域正弦分量，精准捕捉复杂动态
- 在N3V和Google Immersive数据集上显著减少轨迹漂移
- 适合需要长期稳定动态渲染的工业级应用
4D Gaussian Splatting（4DGS）通过高效的4维高斯表示和可并行渲染，在动态三维重建和实时新视角合成方面表现优异。然而，现有4DGS方法依赖单一多项式建模运动，难以应对高频运动成分丰富的复杂动态场景，且因轨迹累积漂移导致长期不稳。为此，本文提出傅里叶运动建模模块：该范式将运动分解为基于频率的正弦分量，同时捕捉低频全局轨迹与高频局部细节，实现对复杂运动模式的精确建模。该方法保持4DGS的实时渲染能力，提升复杂运动拟合度与长期一致性。此外，我们在损失函数中引入运动感知正则化策略，使用频率相关权重抑制高频抖动，同时保留低频运动连贯性。在多场景的N3V与Google Immersive数据集上的大量实验验证了该方法的有效性。
原文摘要 · Abstract (English)
4D Gaussian Splatting (4DGS) excels in dynamic 3D reconstruction and real-time novel view synthesis via efficient 4D Gaussian representations and parallelizable rendering. However, existing 4DGS approaches rely on a single polynomial to model motion, which limits performance in complex dynamic scenes where high-frequency motion components are prevalent, and fails to ensure long-term stability due to cumulative trajectory drift. To address these issues, we propose a Fourier Motion Modeling module: this paradigm decomposes motion into frequency-based sinusoidal components, capturing both low-frequency global trajectories and high-frequency local details to model complex motion patterns accurately. It retains the real-time rendering capability of 4DGS while improving complex motion fitting and long-term coherence. Additionally, we integrate a motion-aware regularization strategy into the loss function: it uses frequency-dependent weights to suppress high-frequency jitter while preserving low-frequency motion coherence. Extensive experiments on N3V and Google Immersive datasets from multiple scenarios demonstrate the effectiveness of our method.
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