摘要
刚体位姿估计旨在获取刚体在相机坐标系下的3D平移信息和3D旋转信息,在自动驾驶、机器人、增强现实等快速发展的领域起着重要作用。现对2017-2021年间的基于深度学习的刚体位姿估计方向具有代表性的研究进行汇总与分析。将刚体位姿估计的方法分为基于坐标、基于关键点和基于模板的方法。将刚体位姿估计任务划分为图像预处理、空间映射或特征匹配、位姿恢复和位姿优化4项子任务,详细介绍每一类方法的子任务实现及其优势和存在的问题。分析刚体位姿估计任务面临的挑战,总结现有解决方案及其优缺点。介绍刚体位姿估计常用的数据集和性能评价指标,并对比分析现有方法在常用数据集上的表现。最后从位姿跟踪、类别级位姿估计等多个角度对未来研究方向进行了展望。
Rigid object pose estimation aims to obtain 3D translation and 3D rotation information of the rigid object in the camera coordinate system,which plays an important role in rapidly developing fields such as autonomous driving,robotics and augmented reality.The representative papers on rigid object pose estimation based on deep learning from 2017 to 2021 are summarized and analyzed.The rigid object pose estimation methods are divided into coordinate-based,keypoints-based and template-based me-thods.The rigid object pose estimation task is divided into four sub-tasks:image preprocessing,spatial mapping or feature ma-tching,pose recovery,and pose optimization.The subtask realization of each method and its advantages and problems are introduced in detail.The challenges of rigid object pose estimation are analyzed,and the existing solutions and their advantages and disadvantages are summarized.Based on the rigid object pose estimation method,the articulated object and deformable object pose estimation are analyzed.The common datasets and performance evaluation indexes of rigid object pose estimation are introduced,and the performance of existing methods on common datasets is compared and analyzed.Finally,the future research directions of pose tracking and class rigid object pose estimation are prospected.
作者
郭楠
李婧源
任曦
GUO Nan;LI Jingyuan;REN Xi(School of Computer Science and Engineering,Northeastern University,Shenyang 110167,China)
出处
《计算机科学》
CSCD
北大核心
2023年第2期178-189,共12页
Computer Science
基金
国家自然科学基金(52130403)
中央高校基本科研业务费专项资金(N2017003)。
关键词
计算机视觉
刚体目标
位姿估计
位姿优化
深度学习
Computer vision
Rigid object
Pose estimation
Pose optimization
Deep learning
