MUTE-SLAM: Real-Time Neural SLAM with Multiple Tri-Plane Hash
  Representations
Yifan Yan Ruomin He Zhenghua Liu
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.