  There is an emerging trend of using neural implicit functions for map
representation in Simultaneous Localization and Mapping (SLAM). Some pioneer
works have achieved encouraging results on RGB-D SLAM. In this paper, we
present a dense RGB SLAM method with neural implicit map representation. To
reach this challenging goal without depth input, we introduce a hierarchical
feature volume to facilitate the implicit map decoder. This design effectively
fuses shape cues across different scales to facilitate map reconstruction. Our
method simultaneously solves the camera motion and the neural implicit map by
matching the rendered and input video frames. To facilitate optimization, we
further propose a photometric warping loss in the spirit of multi-view stereo
to better constrain the camera pose and scene geometry. We evaluate our method
on commonly used benchmarks and compare it with modern RGB and RGB-D SLAM
systems. Our method achieves favorable results than previous methods and even
surpasses some recent RGB-D SLAM methods.The code is at
poptree.github.io/DIM-SLAM/.
