We introduce MUTE-SLAM, a real-time neural RGB-D SLAM system employing multiple tri-plane hash-encodings for efficient scene representation. MUTE-SLAM effectively tracks camera positions and incrementally builds a scalable multi-map representation for both small and large indoor environments. It dynamically allocates sub-maps for newly observed local regions, enabling constraint-free mapping without prior scene information. Unlike traditional grid-based methods, we use three orthogonal axis-aligned planes for hash-encoding scene properties, significantly reducing hash collisions and the number of trainable parameters. This hybrid approach not only speeds up convergence but also enhances the fidelity of surface reconstruction. Furthermore, our optimization strategy concurrently optimizes all sub-maps intersecting with the current camera frustum, ensuring global consistency. Extensive testing on both real-world and synthetic datasets has shown that MUTE-SLAM delivers state-of-the-art surface reconstruction quality and competitive tracking performance across diverse indoor settings. The code will be made public upon acceptance of the paper.

引入MUTE-SLAM，一种实时的神经RGB-D SLAM系统，采用多个三平面哈希编码来进行高效的场景表示。MUTE-SLAM有效地跟踪相机位置并逐步构建可扩展的多地图表示，适用于小型和大型室内环境。该系统动态为新观察到的局部区域分配子地图，实现不需要先验场景信息的无约束建图。与传统的基于网格的方法不同，我们使用三个正交轴对齐平面来进行哈希编码，显著减少哈希冲突和可训练参数数目。这种混合方法不仅加速收敛，还提高了表面重建的保真度。此外，我们的优化策略同时优化了与当前相机视锥体相交的所有子地图，确保全局一致性。在真实世界和合成数据集上进行了广泛测试，表明MUTE-SLAM在各种室内环境中提供了最先进的表面重建质量和具有竞争力的跟踪性能。该论文接受后将公开代码。

MUTE-SLAM：实时神经SLAM与多个三面平面哈希表示