MUTE-SLAM: Real-Time Neural SLAM with Multiple Tri-Plane Hash Representations
<|reference_start|>MUTE-SLAM: Real-Time Neural SLAM with Multiple Tri-Plane Hash Representations: 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. As previous methods often require pre-defined scene boundaries, MUTE-SLAM 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 ensures real-time performance 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 is available at https://github.com/lumennYan/MUTE_SLAM.<|reference_end|>
@article{yan2024mute-slam:,
    title={MUTE-SLAM: Real-Time Neural SLAM with Multiple Tri-Plane Hash
  Representations},
    author={Yifan Yan, Ruomin He, Zhenghua Liu},
    journal={arXiv preprint arXiv:2403.17765},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.17765},
    primaryClass={cs.CV}
}