  In this paper, we introduce Hi-Map, a novel monocular dense mapping approach
based on Neural Radiance Field (NeRF). Hi-Map is exceptional in its capacity to
achieve efficient and high-fidelity mapping using only posed RGB inputs. Our
method eliminates the need for external depth priors derived from e.g., a depth
estimation model. Our key idea is to represent the scene as a hierarchical
feature grid that encodes the radiance and then factorizes it into feature
planes and vectors. As such, the scene representation becomes simpler and more
generalizable for fast and smooth convergence on new observations. This allows
for efficient computation while alleviating noise patterns by reducing the
complexity of the scene representation. Buttressed by the hierarchical
factorized representation, we leverage the Sign Distance Field (SDF) as a proxy
of rendering for inferring the volume density, demonstrating high mapping
fidelity. Moreover, we introduce a dual-path encoding strategy to strengthen
the photometric cues and further boost the mapping quality, especially for the
distant and textureless regions. Extensive experiments demonstrate our method's
superiority in geometric and textural accuracy over the state-of-the-art
NeRF-based monocular mapping methods.
