In the past decade, deep learning (DL) has taken the world by storm. It has produced significant results in a wide variety of applications ranging from self driving cars to natural language processing (NLP). Modern deep learning is built from a number of different algorithms including artificial neural networks (ANN), optimisation algorithms, back-propagation (BP), and varying levels of supervision. Recent advances in GPU hardware, improved availability of large, high quality datasets, and the development of modern training algorithms have all played a pivotal role in the emergence of modern deep learning. These advances have made it easier to train and deploy deeper neural networks that exhibit great generalisation and state-of-the-art, (SOTA), results.
Scene understanding is a critical topic in computer vision. In recent years, semantic segmentation and monocular depth estimation have emerged as two key methods for achieving this goal. The combination of these two tasks enables a sys