MUTE-SLAM: Real-Time Neural SLAM with Multiple Tri-Plane Hash Representations

• URL: http://arxiv.org/abs/2403.17765v2
• Date: Sun, 7 Jul 2024 09:15:52 GMT
• Title: MUTE-SLAM: Real-Time Neural SLAM with Multiple Tri-Plane Hash Representations
• Authors: Yifan Yan, Ruomin He, Zhenghua Liu,
• Abstract summary: 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.
• Score: 6.266208986510979
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 will be made public upon acceptance of the paper.

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