  We propose NEDS-SLAM, an Explicit Dense semantic SLAM system based on 3D
Gaussian representation, that enables robust 3D semantic mapping, accurate
camera tracking, and high-quality rendering in real-time. In the system, we
propose a Spatially Consistent Feature Fusion model to reduce the effect of
erroneous estimates from pre-trained segmentation head on semantic
reconstruction, achieving robust 3D semantic Gaussian mapping. Additionally, we
employ a lightweight encoder-decoder to compress the high-dimensional semantic
features into a compact 3D Gaussian representation, mitigating the burden of
excessive memory consumption. Furthermore, we leverage the advantage of 3D
Gaussian splatting, which enables efficient and differentiable novel view
rendering, and propose a Virtual Camera View Pruning method to eliminate
outlier GS points, thereby effectively enhancing the quality of scene
representations. Our NEDS-SLAM method demonstrates competitive performance over
existing dense semantic SLAM methods in terms of mapping and tracking accuracy
on Replica and ScanNet datasets, while also showing excellent capabilities in
3D dense semantic mapping.
