In recent years, coordinate-based neural implicit representations have shown
promising results for the task of Simultaneous Localization and Mapping (SLAM).
While achieving impressive performance on small synthetic scenes, these methods
often suffer from oversmoothed reconstructions, especially for complex
real-world scenes. In this work, we introduce DNS SLAM, a novel neural RGB-D
semantic SLAM approach featuring a hybrid representation. Relying only on 2D
semantic priors, we propose the first semantic neural SLAM method that trains
class-wise scene representations while providing stable camera tracking at the
same time. Our method integrates multi-view geometry constraints with
image-based feature extraction to improve appearance details and to output
color, density, and semantic class information, enabling many downstream
applications. To further enable real-time tracking, we introduce a lightweight
coarse scene representation which is trained in a self-supervised manner in
latent space. Our experimental results achieve state-of-the-art performance on
both synthetic data and real-world data tracking while maintaining a
commendable operational speed on off-the-shelf hardware. Further, our method
outputs class-wise decomposed reconstructions with better texture capturing
appearance and geometric details.

通过融合 2D 语义先验和多视角几何约束，DNS SLAM 方法提出了一种新型的神经 RGB-D 语义 SLAM 方法，可以在保持稳定相机跟踪的同时，训练类别级别的场景表示，实现颜色、密度和语义类别信息的输出，并在合成数据和实际数据跟踪中取得了最先进的性能，同时在现有硬件上保持了良好的实时跟踪速度，并输出了带有更好的纹理和几何细节的分解重建结果。