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

• URL: http://arxiv.org/abs/2508.04966v1
• Date: Thu, 07 Aug 2025 01:39:29 GMT
• Title: Laplacian Analysis Meets Dynamics Modelling: Gaussian Splatting for 4D Reconstruction
• Authors: Yifan Zhou, Beizhen Zhao, Pengcheng Wu, Hao Wang,
• Abstract summary: We propose a novel dynamic 3DGS framework with hybrid explicit-implicit functions.<n>Our method demonstrates state-of-the-art performance in reconstructing complex dynamic scenes, achieving better reconstruction fidelity.
• Score: 9.911802466255653
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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