Flow4DGS-SLAM：基于光流的4D高斯点划法SLAM
Flow4DGS-SLAM: Optical Flow-Guided 4D Gaussian Splatting SLAM
AI总结：
本文提出基于光流的4D高斯点划法SLAM框架，通过光流引导实现动态环境的高效重建与姿态估计，结合时间一致性模型提升训练效率和动态重建质量。
摘要：
处理动态环境是视觉同时定位与建图（SLAM）中的重大挑战。近期研究将3D高斯点划法（3DGS）与SLAM结合，实现了鲁棒的相机姿态估计和逼真渲染。然而，使用SLAM高效重建静态和动态区域仍具挑战性。本文提出一种高效的动态3DGS SLAM框架，通过光流引导实现。利用输入深度和先验光流，我们首先提出一种类别无关的运动遮罩生成策略，通过拟合相机自身运动模型分解光流。该模块分离动态和静态高斯点，并同时提供光流引导的相机姿态初始化。我们通过显式建模关键帧中的时间中心来加速动态3DGS的训练，这些中心通过3D场景光流先验进行传播，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）建模时间透明度和旋转，以自适应学习复杂动态。实验证明，我们的方法在跟踪、动态重建和训练效率方面均达到最新水平。
英文摘要：
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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