  Neural radiance fields (NeRF) shows powerful performance in novel view
synthesis and 3D geometry reconstruction, but it suffers from critical
performance degradation when the number of known viewpoints is drastically
reduced. Existing works attempt to overcome this problem by employing external
priors, but their success is limited to certain types of scenes or datasets.
Employing monocular depth estimation (MDE) networks, pretrained on large-scale
RGB-D datasets, with powerful generalization capability would be a key to
solving this problem: however, using MDE in conjunction with NeRF comes with a
new set of challenges due to various ambiguity problems exhibited by monocular
depths. In this light, we propose a novel framework, dubbed D\"aRF, that
achieves robust NeRF reconstruction with a handful of real-world images by
combining the strengths of NeRF and monocular depth estimation through online
complementary training. Our framework imposes the MDE network's powerful
geometry prior to NeRF representation at both seen and unseen viewpoints to
enhance its robustness and coherence. In addition, we overcome the ambiguity
problems of monocular depths through patch-wise scale-shift fitting and
geometry distillation, which adapts the MDE network to produce depths aligned
accurately with NeRF geometry. Experiments show our framework achieves
state-of-the-art results both quantitatively and qualitatively, demonstrating
consistent and reliable performance in both indoor and outdoor real-world
datasets. Project page is available at https://ku-cvlab.github.io/DaRF/.
