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.

通过稀疏的三平面编码和分层捆绑调整，我们提出了一种能够在高分辨率上实现快速而高质量跟踪和建图的方法，该方法仅使用了常用三平面参数的 2~4% 的存储空间。