S3-SLAM: Sparse Tri-plane Encoding for Neural Implicit SLAM
<|reference_start|>S3-SLAM: Sparse Tri-plane Encoding for Neural Implicit SLAM: 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.<|reference_end|>
@article{zhang2024s3-slam:,
    title={S3-SLAM: Sparse Tri-plane Encoding for Neural Implicit SLAM},
    author={Zhiyao Zhang, Yunzhou Zhang, Yanmin Wu, Bin Zhao, Xingshuo Wang, Rui
  Tian},
    journal={arXiv preprint arXiv:2404.18284},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.18284},
    primaryClass={cs.CV}
}