VBGS-SLAM：变分贝叶斯高斯点撒SLAM
VBGS-SLAM: Variational Bayesian Gaussian Splatting Simultaneous Localization and Mapping
AI总结：
本文提出VBGS-SLAM，结合高斯点撒地图优化与相机姿态跟踪，通过变分推断提升鲁棒性和精度，实验证明在长序列预测和多场景中表现优异。
摘要：
3D高斯点撒（3DGS）在3D场景建模中表现出色，但现有SLAM变体依赖确定性姿态优化，易受初始化影响且易出现灾难性遗忘。本文提出变分贝叶斯高斯点撒SLAM（VBGS-SLAM），通过生成式概率形式耦合点撒地图优化与相机姿态跟踪。利用多元高斯的共轭性质和变分推断，方法支持高效的闭式更新，并显式维护姿态和场景参数的后验不确定性。该方法在挑战性条件下减少漂移并增强鲁棒性，同时保持3DGS的效率和渲染质量。实验表明，VBGS-SLAM在长序列预测和多样化的合成及现实场景中实现了优越的跟踪性能和鲁棒性，以及高效的高质量新视角合成。
英文摘要：
3D Gaussian Splatting (3DGS) has shown promising results for 3D scene modeling using mixtures of Gaussians, yet its existing simultaneous localization and mapping (SLAM) variants typically rely on direct, deterministic pose optimization against the splat map, making them sensitive to initialization and susceptible to catastrophic forgetting as map evolves. We propose Variational Bayesian Gaussian Splatting SLAM (VBGS-SLAM), a novel framework that couples the splat map refinement and camera pose tracking in a generative probabilistic form. By leveraging conjugate properties of multivariate Gaussians and variational inference, our method admits efficient closed-form updates and explicitly maintains posterior uncertainty over both poses and scene parameters. This uncertainty-aware method mitigates drift and enhances robustness in challenging conditions, while preserving the efficiency and rendering quality of existing 3DGS. Our experiments demonstrate superior tracking performance and robustness in long sequence prediction, alongside efficient, high-quality novel view synthesis across diverse synthetic and real-world scenes.
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。
