拉普拉斯分析遇上动力学建模：用于4D重建的高斯泼溅
Laplacian Analysis Meets Dynamics Modelling: Gaussian Splatting for 4D Reconstruction
AI总结：
针对动态3DGS的频谱冲突问题，提出混合显隐函数框架，含拉普拉斯编码、增强动力学属性及自适应分裂，实现高保真4D重建。
摘要：
虽然3D高斯泼溅（3DGS）在静态场景建模中表现出色，但其向动态场景的扩展引入了重大挑战。现有的动态3DGS方法要么因低秩分解而产生过度平滑，要么因高维网格采样而遭受特征碰撞。这是因为在保留运动细节与维持不同频率下形变一致性之间存在固有的频谱冲突。为解决这些挑战，我们提出了一种具有混合显式-隐式函数的新型动态3DGS框架。我们的方法包含三个关键创新：一种频谱感知的拉普拉斯编码架构，融合了哈希编码和基于拉普拉斯的模块以实现灵活的频率运动控制；一种增强的高斯动力学属性，用于补偿由几何形变引起的光度失真；以及一种由KDTree基元控制引导的自适应高斯分裂策略，以高效查询和优化动态区域。通过大量实验，我们的方法在重建复杂动态场景方面展现了最先进的性能，实现了更好的重建保真度。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
