  Automotive scene understanding under adverse weather conditions raises a
realistic and challenging problem attributable to poor outdoor scene visibility
(e.g. foggy weather). However, because most contemporary scene understanding
approaches are applied under ideal-weather conditions, such approaches may not
provide genuinely optimal performance when compared to established a priori
insights on extreme-weather understanding. In this paper, we propose a complex
but competitive multi-task learning approach capable of performing in real-time
semantic scene understanding and monocular depth estimation under foggy weather
conditions by leveraging both recent advances in adversarial training and
domain adaptation. As an end-to-end pipeline, our model provides a novel
solution to surpass degraded visibility in foggy weather conditions by
transferring scenes from foggy to normal using a GAN-based model. For optimal
performance in semantic segmentation, our model generates depth to be used as
complementary source information with RGB in the segmentation network. We
provide a robust method for foggy scene understanding by training two models
(normal and foggy) simultaneously with shared weights (each model is trained on
each weather condition independently). Our model incorporates RGB colour,
depth, and luminance images via distinct encoders with dense connectivity and
features fusing, and leverages skip connections to produce consistent depth and
segmentation predictions. Using this architectural formulation with light
computational complexity at inference time, we are able to achieve comparable
performance to contemporary approaches at a fraction of the overall model
complexity.
