  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.
