MoD-SLAM: 面向无界三维场景重建的单目稠密建图
MoD-SLAM: Monocular Dense Mapping for Unbounded 3D Scene Reconstruction
AI总结：
针对现有单目SLAM仅适用于有界场景的局限，提出基于高斯无界表示与前端深度监督的MoD-SLAM方法，实现无界场景实时稠密重建，在标准数据集上重建与定位精度最高提升30%和15%。
摘要：
单目SLAM因仅需简单RGB输入、无需复杂传感器约束而受到广泛关注。然而，现有单目SLAM系统均针对有界场景设计，限制了SLAM系统的适用范围。为解决这一局限，我们提出MoD-SLAM，这是首个基于单目NeRF的稠密建图方法，可在无界场景中实现实时三维重建。具体而言，我们引入了一种基于高斯的无界场景表示方法，以解决无边界场景的建图难题，该策略对拓展SLAM的应用场景至关重要。此外，我们在前端设计了深度估计模块，用于提取准确的先验深度值，以监督建图和跟踪过程。通过在跟踪过程中引入鲁棒深度损失项，我们的SLAM系统在大规模场景中实现了更精确的位姿估计。我们在两个标准数据集上的实验表明，MoD-SLAM取得了具有竞争力的性能，与现有最先进的单目SLAM系统相比，其三维重建精度和定位精度分别提升了最高30%和15%。
英文摘要：
Monocular SLAM has received a lot of attention due to its simple RGB inputs and the lifting of complex sensor constraints. However, existing monocular SLAM systems are designed for bounded scenes, restricting the applicability of SLAM systems. To address this limitation, we propose MoD-SLAM, the first monocular NeRF-based dense mapping method that allows 3D reconstruction in real-time in unbounded scenes. Specifically, we introduce a Gaussian-based unbounded scene representation approach to solve the challenge of mapping scenes without boundaries. This strategy is essential to extend the SLAM application. Moreover, a depth estimation module in the front-end is designed to extract accurate priori depth values to supervise mapping and tracking processes. By introducing a robust depth loss term into the tracking process, our SLAM system achieves more precise pose estimation in large-scale scenes. Our experiments on two standard datasets show that MoD-SLAM achieves competitive performance, improving the accuracy of the 3D reconstruction and localization by up to 30% and 15% respectively compared with existing state-of-the-art monocular SLAM systems.
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。
