Simultaneous localization and mapping (SLAM) systems with novel view synthesis capabilities are widely used in computer vision, with applications in augmented reality, robotics, and autonomous driving. However, existing approaches are limited to single-agent operation. Recent work has addressed this problem using a distributed neural scene representation. Unfortunately, existing methods are slow, cannot accurately render real-world data, are restricted to two agents, and have limited tracking accuracy. In contrast, we propose a rigidly deformable 3D Gaussian-based scene representation that dramatically speeds up the system. However, improving tracking accuracy and reconstructing a globally consistent map from multiple agents remains challenging due to trajectory drift and discrepancies across agents' observations. Therefore, we propose new tracking and map-merging mechanisms and integrate loop closure in the Gaussian-based SLAM pipeline. We evaluate MAGiC-SLAM on synthetic and real-world datasets and find it more accurate and faster than the state of the art.
同时定位与建图（Simultaneous Localization and Mapping, SLAM）系统结合新视角合成功能广泛应用于计算机视觉领域，如增强现实、机器人技术和自动驾驶。然而，现有方法局限于单代理操作。最近的研究通过分布式神经场景表示解决了这一问题，但现有方法存在运行速度慢、无法准确渲染真实数据、仅支持两个代理以及跟踪精度有限等问题。 针对这些限制，我们提出了一种基于刚性可变形 3D 高斯的场景表示，大幅提升了系统的运行速度。然而，由于轨迹漂移和代理观测之间的不一致性，提高跟踪精度并从多代理观测中重建全局一致的地图仍然是一个挑战。为此，我们引入了新的跟踪和地图合并机制，并在基于高斯的 SLAM 流水线中集成了闭环检测（loop closure）。 我们在合成和真实数据集上对 MAGiC-SLAM 进行了评估，结果表明，该方法在精度和速度方面均优于现有的最先进方法，展示了在多代理 SLAM 系统中的显著优势。