  In this paper, we look into the problem of estimating per-pixel depth maps
from unconstrained RGB monocular night-time images which is a difficult task
that has not been addressed adequately in the literature. The state-of-the-art
day-time depth estimation methods fail miserably when tested with night-time
images due to a large domain shift between them. The usual photo metric losses
used for training these networks may not work for night-time images due to the
absence of uniform lighting which is commonly present in day-time images,
making it a difficult problem to solve. We propose to solve this problem by
posing it as a domain adaptation problem where a network trained with day-time
images is adapted to work for night-time images. Specifically, an encoder is
trained to generate features from night-time images that are indistinguishable
from those obtained from day-time images by using a PatchGAN-based adversarial
discriminative learning method. Unlike the existing methods that directly adapt
depth prediction (network output), we propose to adapt feature maps obtained
from the encoder network so that a pre-trained day-time depth decoder can be
directly used for predicting depth from these adapted features. Hence, the
resulting method is termed as "Adversarial Domain Feature Adaptation (ADFA)"
and its efficacy is demonstrated through experimentation on the challenging
Oxford night driving dataset. Also, The modular encoder-decoder architecture
for the proposed ADFA method allows us to use the encoder module as a feature
extractor which can be used in many other applications. One such application is
demonstrated where the features obtained from our adapted encoder network are
shown to outperform other state-of-the-art methods in a visual place
recognition problem, thereby, further establishing the usefulness and
effectiveness of the proposed approach.
