Frequency-Aware Uncertainty Gaussian Splatting for Dynamic Scene Reconstruction

Mingwen Shao, Yuanjian Qiao, Kai Zhang, Lingzhuang Meng

Abstract: 3D Gaussian splatting has recently achieved remarkable progress in dynamic scene reconstruction. However, there remain two practical challenges: (1) Existing methods typically employ a strict point-wise deformation structure to model dynamic attributes, while neglecting the uncertain motion correlation in local space, leading to inferior adaptability to complex scenes. (2) The inherent low-frequency bias properties of Gaussians often lead to blurring artifacts due to the insufficient high-frequency learning of variable motions. To address these challenges, we propose a novel Frequency-aware Uncertainty Gaussian Splatting, termed FUGS, for adaptively reconstructing dynamic scenes in the Fourier space. Specifically, we design an Uncertainty-aware Deformation Model (UDM) that explicitly models motion attributes using learnable uncertainty relations with neighboring Gaussian points. Such a paradigm is capable of facilitating temporal and spatial motion correlation learning, thereby enabling flexible Gaussian deformations. Subsequently, a Dynamic Spectrum Regularization (DSR) is developed to perform coarse-to-fine Gaussian densification through low-to-high frequency filtering. By weighting the gradient with frequency distance, the Gaussian attribute is adaptively adjusted according to the scene complexity. Benefiting from the flexible optimization, our method achieves high-fidelity reconstruction of complex scenes while enjoying real-time rendering. Extensive experiments on synthetic and real-world datasets show that our FUGS exhibits significant superiority over state-of-the-art methods. The code will be available at https://github.com/KevinJoee/GS.