解决动态场景3D高斯点云重建中的模糊与冲突问题。
Laplacian Analysis Meets Dynamics Modelling: Gaussian Splatting for 4D Reconstruction
- 用谱感知拉普拉斯编码融合哈希与频域控制，灵活调节运动频率。
- 提升高斯动态属性，减少形变导致的光照失真。
- 基于KDTree自适应分裂，高效优化动态区域，适合复杂动态场景重建。
尽管3D高斯点云（3DGS）在静态场景建模中表现优异，但其扩展到动态场景时面临显著挑战。现有动态3DGS方法或因低秩分解导致过度平滑，或因高维网格采样引发特征碰撞，根源在于运动细节保留与形变一致性之间的频谱冲突。为此，我们提出一种新型动态3DGS框架，采用显式-隐式混合函数。核心创新包括：一种谱感知拉普拉斯编码架构，结合哈希编码与基于拉普拉斯的模块，实现灵活的频率运动控制；增强的高斯动态属性，补偿几何形变引起的光度失真；以及基于KDTree的自适应高斯分裂策略，高效查询与优化动态区域。大量实验表明，该方法在复杂动态场景重建中达到当前最优性能，显著提升重建保真度。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。