MMD-SLAM：结构增强的多元高斯分布引导视觉SLAM
MMD-SLAM: Structure-Enhanced Multi-Meta Gaussian Distribution-Guided Visual SLAM
AI总结：
提出MMD-SLAM，利用亚特兰大世界假设引导多元高斯表示，通过点线融合、主导方向编码和高斯进化策略，提升视觉SLAM的跟踪精度与建图质量。
摘要：
3D高斯泼溅（3DGS）显著提升了新视角合成和高保真场景重建，扩展了基于3DGS的视觉同步定位与建图（SLAM）方法的潜力。然而，大多数现有系统未能充分利用底层结构信息，这限制了渲染质量并常常导致地图不一致。为了解决这些限制，我们提出了MMD-SLAM，一个结构增强的视觉SLAM框架，利用亚特兰大世界（AW）假设来引导多元高斯表示以实现逼真的建图。首先，我们引入了一种点线融合策略用于位姿优化，其中3D线段被纳入以提高跟踪鲁棒性并为建图提供额外约束。其次，我们设计了一种具有主导方向的多元高斯表示，显式编码来自AW假设的结构先验。最后，我们提出了一种高斯进化策略，该策略适应场景几何并将结构线索融入全局优化。大量实验表明，这些创新使MMD-SLAM在跟踪精度和建图质量方面均达到了最先进的性能。例如，与MonoGS相比，我们的方法在ScanNet上实现了48.56%的ATE RMSE降低，在Replica上实现了5.71%的PSNR提升。
英文摘要：
3D Gaussian Splatting (3DGS) has significantly boosted novel view synthesis and high-fidelity scene reconstruction, expanding the potential of 3DGS-based Visual Simultaneous Localization and Mapping (SLAM) methods. However, most existing systems fail to fully exploit the underlying structural information, which limits rendering quality and often leads to inconsistent maps. To address these limitations, we propose MMD-SLAM, a structure-enhanced Visual SLAM framework that leverages the Atlanta World (AW) assumption to guide a Multi-Meta Gaussian representation for photorealistic mapping. First, we introduce a point-line fusion strategy for pose optimization, where 3D line segments are incorporated to improve tracking robustness and provide additional constraints for mapping. Second, we design a Multi-Meta Gaussian representation with dominant directions, explicitly encoding structural priors from the AW hypothesis. Finally, we propose a Gaussian evolution strategy that adapts to scene geometry and incorporates structural cues into global optimization. Extensive experiments demonstrate that these innovations enable MMD-SLAM to achieve state-of-the-art performance in both tracking accuracy and mapping quality. e.g., our method achieves a 48.56% reduction in ATE RMSE on ScanNet and a 5.71% improvement in PSNR on Replica, compared with MonoGS.
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。
