Laplacian Analysis Meets Dynamics Modelling: Gaussian Splatting for 4D Scene Reconstruction

Yifan Zhou, Beizhen ZHAO, Pengcheng Wu, Hao Wang

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.

Primary Area: applications to computer vision, audio, language, and other modalities

Submission Number: 10239