  The capabilities of monocular depth estimation (MDE) models are limited by
the availability of sufficient and diverse datasets. In the case of MDE models
for autonomous driving, this issue is exacerbated by the linearity of the
captured data trajectories. We propose a NeRF-based data augmentation pipeline
to introduce synthetic data with more diverse viewing directions into training
datasets and demonstrate the benefits of our approach to model performance and
robustness. Our data augmentation pipeline, which we call "NeRFmentation",
trains NeRFs on each scene in the dataset, filters out subpar NeRFs based on
relevant metrics, and uses them to generate synthetic RGB-D images captured
from new viewing directions. In this work, we apply our technique in
conjunction with three state-of-the-art MDE architectures on the popular
autonomous driving dataset KITTI, augmenting its training set of the Eigen
split. We evaluate the resulting performance gain on the original test set, a
separate popular driving set, and our own synthetic test set.
