  We introduce a high-fidelity neural implicit dense visual Simultaneous
Localization and Mapping (SLAM) system, termed DF-SLAM. In our work, we employ
dictionary factors for scene representation, encoding the geometry and
appearance information of the scene as a combination of basis and coefficient
factors. Compared to neural implicit dense visual SLAM methods that directly
encode scene information as features, our method exhibits superior scene detail
reconstruction capabilities and more efficient memory usage, while our model
size is insensitive to the size of the scene map, making our method more
suitable for large-scale scenes. Additionally, we employ feature integration
rendering to accelerate color rendering speed while ensuring color rendering
quality, further enhancing the real-time performance of our neural SLAM method.
Extensive experiments on synthetic and real-world datasets demonstrate that our
method is competitive with existing state-of-the-art neural implicit SLAM
methods in terms of real-time performance, localization accuracy, and scene
reconstruction quality. Our source code is available at
https://github.com/funcdecl/DF-SLAM.
