用光流引导4D高斯溅射，高效重建动态场景的定位与映射。
Flow4DGS-SLAM: Optical Flow-Guided 4D Gaussian Splatting SLAM
- 基于光流和相机运动模型生成无类别动态掩码，分离动静态高斯点。
- 关键帧上显式建模动态高斯时序中心，提升重建速度与精度。
- 适合需要实时动态场景重建的自动驾驶与机器人应用。
处理动态环境是视觉同步定位与地图构建（SLAM）中的重要研究挑战。近期工作将3D高斯溅射（3DGS）与SLAM结合，实现了鲁棒的相机位姿估计和逼真的渲染效果。然而，高效重建静态与动态区域仍具挑战。本文提出一种由光流引导的高效动态3DGS SLAM框架。通过输入深度图和先验光流，我们提出一种无类别运动掩码生成策略，通过拟合相机自身运动模型来分解光流，从而分离动态与静态高斯点，并同时提供光流引导的位姿初始化。我们通过在关键帧显式建模动态高斯的时间中心，提升了动态3DGS的训练速度；这些中心利用3D场景流先验进行传播，并通过自适应插入策略动态初始化。此外，我们使用高斯混合模型（GMM）建模时间透明度与旋转，以自适应学习复杂动态变化。实验结果表明，本方法在跟踪、动态重建和训练效率方面均达到当前最优水平。
原文摘要 · Abstract (English)
Handling the dynamic environments is a significant research challenge in Visual Simultaneous Localization and Mapping (SLAM). Recent research combines 3D Gaussian Splatting (3DGS) with SLAM to achieve both robust camera pose estimation and photorealistic renderings. However, using SLAM to efficiently reconstruct both static and dynamic regions remains challenging. In this work, we propose an efficient framework for dynamic 3DGS SLAM guided by optical flow. Using the input depth and prior optical flow, we first propose a category-agnostic motion mask generation strategy by fitting a camera ego-motion model to decompose the optical flow. This module separates dynamic and static Gaussians and simultaneously provides flow-guided camera pose initialization. We boost the training speed of dynamic 3DGS by explicitly modeling their temporal centers at keyframes. These centers are propagated using 3D scene flow priors and are dynamically initialized with an adaptive insertion strategy. Alongside this, we model the temporal opacity and rotation using a Gaussian Mixture Model (GMM) to adaptively learn the complex dynamics. The empirical results demonstrate our state-of-the-art performance in tracking, dynamic reconstruction, and training efficiency.
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