ISSN :2582-9793
Negar Nejatishahidin and Pooya Fayyazsanavi
Adv. Artif. Intell. Mach. Learn., 2 (4):588-613
Negar Nejatishahidin : George Mason University
Pooya Fayyazsanavi : Research assistant
DOI: 10.54364/AAIML.2022.1141
Article History: Received on: 05-Dec-22, Accepted on: 18-Dec-22, Published on: 31-Dec-22
Corresponding Author: Negar Nejatishahidin
Email: nnejatis@gmu.edu
Citation: Negar Nejatishahidin (2022). Review on 6D Object Pose Estimation with the focus on Indoor Scene Understanding. Adv. Artif. Intell. Mach. Learn., 2 (4 ):588-613
6D object pose estimation problem has been extensively studied in the field of Computer Vision and Robotics. It has wide range of applications such as robot manipulation, augmented reality, and 3D scene understanding. With the advent of Deep Learning, many breakthroughs have been made; however, approaches continue to struggle when they encounter unseen instances, new categories, or real-world challenges such as cluttered backgrounds and occlusions. In this study, we will explore the available methods based on input modality, problem formulation, and whether it is a category-level or instance-level approach. As a part of our discussion, we will focus on how 6D object pose estimation can be used for understanding 3D scenes.