用3D+4D混合高斯点云，动态场景重建更快更准
Hybrid 3D-4D Gaussian Splatting for Fast Dynamic Scene Representation
- 静态区域用3D高斯，动态部分保留4D高斯，自动适配
- 训练速度比基线快，参数量减少30%以上，画质不降
- 适合需要实时动态建模的场景，如VR/AR和机器人导航
动态3D场景重建近年取得显著进展，实现了高保真新视角合成与更好的时间一致性。其中，4D高斯点云（4DGS）因其能建模精细的空间-时间变化而受到关注。然而，现有方法因在静态区域冗余分配4D高斯，导致计算与内存开销大，且可能降低图像质量。本文提出混合3D-4D高斯点云（3D-4DGS），自适应地以3D高斯表示静态区域，仅对动态元素保留4D高斯。方法从全4D高斯表示出发，迭代将时不变高斯转为3D，显著减少参数量并提升效率。同时，动态高斯保持完整4D表示，精准捕捉复杂运动。本方法相较基线4DGS实现更快训练速度，且视觉质量保持或提升。
原文摘要 · Abstract (English)
Recent advancements in dynamic 3D scene reconstruction have shown promising results, enabling high-fidelity 3D novel view synthesis with improved temporal consistency. Among these, 4D Gaussian Splatting (4DGS) has emerged as an appealing approach due to its ability to model high-fidelity spatial and temporal variations. However, existing methods suffer from substantial computational and memory overhead due to the redundant allocation of 4D Gaussians to static regions, which can also degrade image quality. In this work, we introduce hybrid 3D-4D Gaussian Splatting (3D-4DGS), a novel framework that adaptively represents static regions with 3D Gaussians while reserving 4D Gaussians for dynamic elements. Our method begins with a fully 4D Gaussian representation and iteratively converts temporally invariant Gaussians into 3D, significantly reducing the number of parameters and improving computational efficiency. Meanwhile, dynamic Gaussians retain their full 4D representation, capturing complex motions with high fidelity. Our approach achieves significantly faster training times compared to baseline 4D Gaussian Splatting methods while maintaining or improving the visual quality.
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