Title: Laplacian Analysis Meets Dynamics Modelling: Gaussian Splatting for 4D Scene Reconstruction
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Deformable Gaussian Splatting, 4D reconstruction, Novel view synthesis, Laplacian Transformation

Abstract: While 3D Gaussian Splatting (3DGS) excels in static scene modeling, its extension to dynamic scenes introduces significant challenges.
Existing dynamic 3DGS methods suffer from either over-smoothing due to low-rank decomposition or feature collision from high-dimensional grid sampling. 
This is because of the inherent spectral conflicts between preserving motion details and maintaining deformation consistency at different frequency. 
To address these challenges, we propose a novel dynamic 3DGS framework with hybrid explicit-implicit functions. 
Our approach contains three key innovations: 
a spectral-aware Laplacian encoding architecture which merges Hash encoding and Laplacian-based module for flexible frequency motion control, 
an enhanced Gaussian dynamics attribute that compensates for photometric distortions caused by geometric deformation,
and an adaptive Gaussian split strategy guided by KDTree-based primitive control to efficiently query and optimize dynamic areas.
Through extensive experiments, our method demonstrates state-of-the-art performance in reconstructing complex dynamic scenes, achieving better reconstruction fidelity.