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.

翻译：计算机视野和机器人领域对6D对象构成的估算问题进行了广泛研究,其应用范围广泛,如机器人操纵、扩大现实和3D场景理解。随着深层学习的到来,已经取得了许多突破;然而,当遇到看不见的情况、新的类别或现实世界的挑战,如背景和排斥等时,各种方法仍然在挣扎。在这项研究中,我们将探索基于投入模式、问题表述的现有方法,以及它是分类或实例层面的方法。作为我们讨论的一部分,我们将侧重于如何利用6D对象构成的估算来理解3D场景。