S3-SLAM: Sparse Tri-plane Encoding for Neural Implicit SLAM
This work addresses efficiency challenges in neural implicit SLAM for robotics and AR/VR applications, representing an incremental improvement over existing methods.
The paper tackles the trade-off between performance and parameter count in neural implicit SLAM by proposing sparse tri-plane encoding, which reduces parameters from 100MB to 2-4MB while achieving competitive scene reconstruction at resolutions up to 512.
With the emergence of Neural Radiance Fields (NeRF), neural implicit representations have gained widespread applications across various domains, including simultaneous localization and mapping. However, current neural implicit SLAM faces a challenging trade-off problem between performance and the number of parameters. To address this problem, we propose sparse tri-plane encoding, which efficiently achieves scene reconstruction at resolutions up to 512 using only 2~4% of the commonly used tri-plane parameters (reduced from 100MB to 2~4MB). On this basis, we design S3-SLAM to achieve rapid and high-quality tracking and mapping through sparsifying plane parameters and integrating orthogonal features of tri-plane. Furthermore, we develop hierarchical bundle adjustment to achieve globally consistent geometric structures and reconstruct high-resolution appearance. Experimental results demonstrate that our approach achieves competitive tracking and scene reconstruction with minimal parameters on three datasets. Source code will soon be available.