Affiliations: [a] ISMAC – Higher Institute of Audiovisual and Film Professions, Rabat, Morocco | [b] Hassan First University of Settat, Faculty of Sciences and Techniques, Computer, Networks, Mobility and Modeling Laboratory: IR2M, 26000 – Settat, Morocco
Correspondence:
[*]
Corresponding author: Houssam Halmaoui, ISMAC – Higher Institute of Audiovisual and Film Professions, Rabat, Morocco. E-mail: houssam.halmaoui@gmail.com.
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.
Keywords: Feature detection, feature descriptor, image matching, handcrafted and learned features, 3D rendering, augmented reality
