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 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
this https URL

引入了一种高保真的神经隐式密集视觉同时定位与建图（SLAM）系统，名为 DF-SLAM。我们利用字典因子对场景进行表示，将场景的几何和外观信息编码为基础和系数因子的组合。与直接将场景信息编码为特征的神经隐式 SLAM 方法相比，我们的方法具有更优秀的场景细节重建能力和更高效的内存使用，同时我们的模型大小对场景地图的大小不敏感，使得我们的方法更适用于大规模场景。此外，我们采用特征集成渲染来加速颜色渲染速度并确保颜色渲染质量，进一步增强了我们的神经 SLAM 方法的实时性能。对合成和真实数据集进行的大量实验证明，我们的方法在实时性能、定位精度和场景重建质量方面与现有最先进的神经隐式 SLAM 方法具有竞争力。我们的源代码可以在此 https URL 获取。