  Neural implicit representations have recently become popular in simultaneous
localization and mapping (SLAM), especially in dense visual SLAM. However,
previous works in this direction either rely on RGB-D sensors, or require a
separate monocular SLAM approach for camera tracking and do not produce
high-fidelity dense 3D scene reconstruction. In this paper, we present
NICER-SLAM, a dense RGB SLAM system that simultaneously optimizes for camera
poses and a hierarchical neural implicit map representation, which also allows
for high-quality novel view synthesis. To facilitate the optimization process
for mapping, we integrate additional supervision signals including
easy-to-obtain monocular geometric cues and optical flow, and also introduce a
simple warping loss to further enforce geometry consistency. Moreover, to
further boost performance in complicated indoor scenes, we also propose a local
adaptive transformation from signed distance functions (SDFs) to density in the
volume rendering equation. On both synthetic and real-world datasets we
demonstrate strong performance in dense mapping, tracking, and novel view
synthesis, even competitive with recent RGB-D SLAM systems.
