聚焦室内场景理解的6D物体位姿估计综述
Review on 6D Object Pose Estimation with the focus on Indoor Scene Understanding
AI总结：
本文综述了6D物体位姿估计方法，按输入模态、问题表述和类别级/实例级进行分类，并重点探讨其在室内3D场景理解中的应用。
摘要：
6D物体位姿估计问题已在计算机视觉和机器人领域得到广泛研究，其应用范围广泛，包括机器人操作、增强现实和3D场景理解。随着深度学习的出现，该领域取得了许多突破；然而，现有方法在遇到未见过的实例、新类别或现实世界挑战（如杂乱背景和遮挡）时仍然面临困难。在本研究中，我们将根据输入模态、问题表述以及是类别级还是实例级方法来探讨现有方法。作为讨论的一部分，我们将重点关注6D物体位姿估计如何用于理解3D场景。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
