拥抱动态：面向动态环境的4D高斯点划SLAM
Embracing Dynamics: Dynamics-aware 4D Gaussian Splatting SLAM
AI总结：
D4DGS-SLAM通过引入4DGS地图表示，解决动态环境中SLAM的定位与地图重建问题，采用动态感知模块和不同正则化项提升动态场景重建质量。
摘要：
同时定位与建图（SLAM）技术近期通过3D高斯点划（3DGS）实现的实时、高保真渲染能力，已实现了逼真映射能力。然而，由于场景的静态表示，基于3DGS的SLAM在动态环境中面临姿态漂移和无法重建准确地图的问题。为解决此问题，我们提出了D4DGS-SLAM，这是首个基于4DGS地图表示的SLAM方法，用于动态环境。通过将时间维度纳入场景表示，D4DGS-SLAM能够实现高质量的动态场景重建。利用动态感知的InfoModule，我们可以获得场景点的动力学、可视性和可靠性，并据此过滤出不稳定动态点以进行跟踪。在优化高斯点时，我们对具有不同动态特征的高斯分布应用不同的各向同性正则化项。在真实世界动态场景数据集上的实验结果表明，我们的方法在摄像机姿态跟踪和地图质量方面均优于最先进的方法。
英文摘要：
Simultaneous localization and mapping (SLAM) technology has recently achieved photorealistic mapping capabilities thanks to the real-time, high-fidelity rendering enabled by 3D Gaussian Splatting (3DGS). However, due to the static representation of scenes, current 3DGS-based SLAM encounters issues with pose drift and failure to reconstruct accurate maps in dynamic environments. To address this problem, we present D4DGS-SLAM, the first SLAM method based on 4DGS map representation for dynamic environments. By incorporating the temporal dimension into scene representation, D4DGS-SLAM enables high-quality reconstruction of dynamic scenes. Utilizing the dynamics-aware InfoModule, we can obtain the dynamics, visibility, and reliability of scene points, and filter out unstable dynamic points for tracking accordingly. When optimizing Gaussian points, we apply different isotropic regularization terms to Gaussians with varying dynamic characteristics. Experimental results on real-world dynamic scene datasets demonstrate that our method outperforms state-of-the-art approaches in both camera pose tracking and map quality.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
