  Neural implicit representations have recently demonstrated considerable
potential in the field of visual simultaneous localization and mapping (SLAM).
This is due to their inherent advantages, including low storage overhead and
representation continuity. However, these methods necessitate the size of the
scene as input, which is impractical for unknown scenes. Consequently, we
propose NeB-SLAM, a neural block-based scalable RGB-D SLAM for unknown scenes.
Specifically, we first propose a divide-and-conquer mapping strategy that
represents the entire unknown scene as a set of sub-maps. These sub-maps are a
set of neural blocks of fixed size. Then, we introduce an adaptive map growth
strategy to achieve adaptive allocation of neural blocks during camera tracking
and gradually cover the whole unknown scene. Finally, extensive evaluations on
various datasets demonstrate that our method is competitive in both mapping and
tracking when targeting unknown environments.
