提出分治式双层次优化，提升动态场景4D高斯渲染质量
Divide-and-Conquer: Dual-Hierarchical Optimization for Semantic 4D Gaussian Spatting
- 分治策略分离静态与动态部分的高斯优化流程
- 在真实与合成数据上均超越基线方法，减少纹理复杂场景的失真
- 适合需要高质量动态3D重建的应用场景
语义4D高斯可用于重建和理解动态场景，其时间变化特性超越静态场景。直接将静态方法应用于动态场景会忽略时间特征，导致效果不佳。现有基于高斯点阵的方法对动态场景关注较少，因统一更新策略难以区分静态与动态高斯，易引入显著伪影和噪声。本文提出双层次优化（DHO），采用分治思想，包含层次化高斯流与层次化高斯引导。前者实现静态与动态渲染及特征的有效分离；后者缓解纹理复杂场景中动态前景渲染失真的问题。大量实验表明，本方法在合成与真实世界数据集上均持续优于基线，支持多种下游任务。
原文摘要 · Abstract (English)
Semantic 4D Gaussians can be used for reconstructing and understanding dynamic scenes, with temporal variations than static scenes. Directly applying static methods to understand dynamic scenes will fail to capture the temporal features. Few works focus on dynamic scene understanding based on Gaussian Splatting, since once the same update strategy is employed for both dynamic and static parts, regardless of the distinction and interaction between Gaussians, significant artifacts and noise appear. We propose Dual-Hierarchical Optimization (DHO), which consists of Hierarchical Gaussian Flow and Hierarchical Gaussian Guidance in a divide-and-conquer manner. The former implements effective division of static and dynamic rendering and features. The latter helps to mitigate the issue of dynamic foreground rendering distortion in textured complex scenes. Extensive experiments show that our method consistently outperforms the baselines on both synthetic and real-world datasets, and supports various downstream tasks. Project Page: https://sweety-yan.github.io/DHO.
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