  Due to the lack of training labels and the difficulty of annotating, dealing
with adverse driving conditions such as nighttime has posed a huge challenge to
the perception system of autonomous vehicles. Therefore, adapting knowledge
from a labelled daytime domain to an unlabelled nighttime domain has been
widely researched. In addition to labelled daytime datasets, existing nighttime
datasets usually provide nighttime images with corresponding daytime reference
images captured at nearby locations for reference. The key challenge is to
minimize the performance gap between the two domains. In this paper, we propose
LoopDA for domain adaptive nighttime semantic segmentation. It consists of
self-loops that result in reconstructing the input data using predicted
semantic maps, by rendering them into the encoded features. In a warm-up
training stage, the self-loops comprise of an inner-loop and an outer-loop,
which are responsible for intra-domain refinement and inter-domain alignment,
respectively. To reduce the impact of day-night pose shifts, in the later
self-training stage, we propose a co-teaching pipeline that involves an offline
pseudo-supervision signal and an online reference-guided signal `DNA'
(Day-Night Agreement), bringing substantial benefits to enhance nighttime
segmentation. Our model outperforms prior methods on Dark Zurich and Nighttime
Driving datasets for semantic segmentation. Code and pretrained models are
available at https://github.com/fy-vision/LoopDA.

Computer Vision and Pattern Recognition