S3-SLAM: Sparse Tri-plane Encoding for Neural Implicit SLAM

arXiv:2404.18284v1 Announce Type: new
Abstract: 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 …

abstract applications arxiv cs.cv current domains emergence encoding fields however localization mapping nerf neural radiance fields parameters performance plane slam trade trade-off type