用高斯点分解动静元素，实现无干扰的动态场景三维重建。
DeGauss: Dynamic-Static Decomposition with Gaussian Splatting for Distractor-free 3D Reconstruction
- 分离动态与静态物体，分别用高斯点建模
- 在多个真实场景数据集上超越现有方法
- 适合复杂交互环境下的通用三维重建
从真实世界捕获中重建干净、无干扰的三维场景仍是一项重大挑战，尤其在高度动态且杂乱的场景中，如第一人称视频。为此，我们提出 DeGauss，一种基于动态-静态高斯点分裂设计的简单且鲁棒的自监督框架。DeGauss 使用前景高斯点建模动态元素，背景高斯点建模静态内容，通过概率掩码协调二者组合，实现独立但互补的优化。该方法在多种真实场景下表现稳健，涵盖日常图像集合到长时间动态第一人称视频，无需复杂启发式或大量监督。在 NeRF-on-the-go、ADT、AEA、Hot3D 及 EPIC-Fields 等基准测试中，DeGauss 均持续优于现有方法，为高动态、强交互环境中的可泛化、无干扰三维重建设立了新基准。
原文摘要 · Abstract (English)
Reconstructing clean, distractor-free 3D scenes from real-world captures remains a significant challenge, particularly in highly dynamic and cluttered settings such as egocentric videos. To tackle this problem, we introduce DeGauss, a simple and robust self-supervised framework for dynamic scene reconstruction based on a decoupled dynamic-static Gaussian Splatting design. DeGauss models dynamic elements with foreground Gaussians and static content with background Gaussians, using a probabilistic mask to coordinate their composition and enable independent yet complementary optimization. DeGauss generalizes robustly across a wide range of real-world scenarios, from casual image collections to long, dynamic egocentric videos, without relying on complex heuristics or extensive supervision. Experiments on benchmarks including NeRF-on-the-go, ADT, AEA, Hot3D, and EPIC-Fields demonstrate that DeGauss consistently outperforms existing methods, establishing a strong baseline for generalizable, distractor-free 3D reconstructionin highly dynamic, interaction-rich environments. Project page: https://batfacewayne.github.io/DeGauss.io/
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