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.

翻译：神经隐式表示凭借其存储开销低、表示连续等固有优势，近年来在视觉同步定位与建图（SLAM）领域展现出巨大潜力。然而，现有方法通常需要预先获知场景尺度作为输入，这在未知场景中并不现实。为此，我们提出NeB-SLAM——一种面向未知场景、基于神经块的可扩展RGB-D SLAM系统。具体而言，我们首先提出一种分治建图策略，将整个未知场景表示为若干子地图的集合，这些子地图由一组固定尺寸的神经块构成。随后，我们引入自适应地图增长策略，在相机跟踪过程中实现神经块的自适应分配，从而逐步覆盖整个未知场景。最后，在多个数据集上的大量实验表明，在面向未知环境时，我们的方法在建图与跟踪性能方面均具有竞争力。