  In recent years, the paradigm of neural implicit representations has gained
substantial attention in the field of Simultaneous Localization and Mapping
(SLAM). However, a notable gap exists in the existing approaches when it comes
to scene understanding. In this paper, we introduce NIS-SLAM, an efficient
neural implicit semantic RGB-D SLAM system, that leverages a pre-trained 2D
segmentation network to learn consistent semantic representations.
Specifically, for high-fidelity surface reconstruction and spatial consistent
scene understanding, we combine high-frequency multi-resolution
tetrahedron-based features and low-frequency positional encoding as the
implicit scene representations. Besides, to address the inconsistency of 2D
segmentation results from multiple views, we propose a fusion strategy that
integrates the semantic probabilities from previous non-keyframes into
keyframes to achieve consistent semantic learning. Furthermore, we implement a
confidence-based pixel sampling and progressive optimization weight function
for robust camera tracking. Extensive experimental results on various datasets
show the better or more competitive performance of our system when compared to
other existing neural dense implicit RGB-D SLAM approaches. Finally, we also
show that our approach can be used in augmented reality applications. Project
page:
\href{https://zju3dv.github.io/nis_slam}{https://zju3dv.github.io/nis\_slam}.
