Laplacian Analysis Meets Dynamics Modelling: Gaussian Splatting for 4D Reconstruction
This work addresses the problem of dynamic 3D reconstruction for computer vision and graphics applications, representing an incremental improvement over existing dynamic 3DGS methods.
The paper tackles the challenge of extending 3D Gaussian Splatting to dynamic scenes, which often leads to over-smoothing or feature collision, by proposing a novel framework with hybrid explicit-implicit functions, achieving state-of-the-art performance in reconstructing complex dynamic scenes with better fidelity.
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.