Review on 6D Object Pose Estimation with the focus on Indoor Scene Understanding
It addresses the problem of robust 6D pose estimation for robotics and AR, but is incremental as it synthesizes existing research.
This paper reviews 6D object pose estimation methods, focusing on their application to indoor scene understanding, and discusses challenges like unseen instances and occlusions.
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.