Feature matching for 3D AR: Review from handcrafted methods to deep learning

Abstract

3D augmented reality (AR) has a photometric aspect of 3D rendering and a geometric aspect of camera tracking. In this paper, we will discuss the second aspect, which involves feature matching for stable 3D object insertion. We present the different types of image matching approaches, starting from handcrafted feature algorithms and machine learning methods, to recent deep learning approaches using various types of CNN architectures, and more modern end-to-end models. A comparison of these methods is performed according to criteria of real time and accuracy, to allow the choice of the most relevant methods for a 3D AR system.