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.
@article{arxiv.2404.18284,
title = {S3-SLAM: Sparse Tri-plane Encoding for Neural Implicit SLAM},
author = {Zhiyao Zhang and Yunzhou Zhang and Yanmin Wu and Bin Zhao and Xingshuo Wang and Rui Tian},
journal= {arXiv preprint arXiv:2404.18284},
year = {2024}
}