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.

提出了一种用于未知场景的神经分块可伸缩 RGB-D SLAM 方法 NeB-SLAM，通过将整个未知场景表示为一组固定大小的神经分块子图，并采用自适应的地图增长策略在摄像机跟踪过程中逐渐覆盖整个未知场景，对各种数据集进行广泛评估，证明了该方法在未知环境的建图和跟踪方面具有竞争力。