%0 Journal Article
%T 基于深度学习的6D位姿估计方法最新研究<br>Recent Research on 6D Pose Estimation Method Based on Deep Learning
%A 杨涵
%A 陶佳林
%A 怀珍豪
%J Software Engineering and Applications
%P 1319-1330
%@ 2325-2278
%D 2022
%I Hans Publishing
%R 10.12677/SEA.2022.116135
%X 6D位姿估计技术在工业机器人、虚拟现实和餐饮服务等领域已经成为关键性的技术。它的发展逐渐由离线方式发展到端到端的方式。这主要因为随着深度学习技术的发展，促进了6D位姿估计理论不断地完善，进一步导致了对该方面的技术总结和时效性不强。因此，本文基于最新的6D位姿估计技术的方法进行调研。在本文中，我们首先介绍了6D位姿估计技术的评估指标、数据集和6D位姿估计方法。其中6D位姿估计方法以数据的输入方式进行划分，我们将方法划分为基于D、RGB和RGBD的方法。最后，我们就这些各类方法给出了我们的启发和未来可能的研究方向，希望给予相关人员一定的帮助。<br />
6D pose estimation technology has become critical in industrial robotics, virtual reality, and food service areas. It has gradually evolved from an offline approach to an end-to-end approach. This is mainly due to the fact that with the development of deep learning techniques, it has facilitated the continuous improvement of 6D pose estimation theory, which has further led to a poor summary and timeliness of the techniques in this area. Therefore, this paper investigates the approach based on the latest 6D pose estimation techniques. In this paper, we first introduce the evaluation metrics, datasets, and 6D pose estimation methods for 6D pose estimation techniques. Where the 6D pose estimation methods are classified by the input method of the data, we classify the methods into D-based, RGB, and RGBD-based methods. Finally, we give our inspiration and possible future research directions on these various methods, which we hope can help related people.
%K 深度学习，位姿估计，RGB，RGBD<br>Deep Learning
%K Pose Estimation
%K RGB
%K RGBD
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=59648