.png){kind=icon}
返回
Deep Learning-Based Object Pose Estimation: A Comprehensive Survey
.png){kind=icon}
PRE
AI
DOI：10.1007/s11263-025-02646-6.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Object pose estimation is a fundamental computer vision problem with broad applications in augmented reality and robotics. Over the past decade, deep learning models, due to their superior accuracy and robustness, have increasingly supplanted conventional algorithms reliant on engineered point pair features. Nevertheless, several challenges persist in contemporary methods, including their dependency on labeled training data, model compactness, robustness under challenging conditions, and their ability to generalize to novel unseen objects. A recent survey discussing the progress made on different aspects of this area, outstanding challenges, and promising future directions, is missing. To fill this gap, we discuss the recent advances in deep learning-based object pose estimation, covering all three formulations of the problem, i.e., instance-level, category-level, and unseen (including both instance-unseen and category-unseen cases) object pose estimation. Our survey also covers multiple input data modalities, degrees-of-freedom of output poses, object properties, and downstream tasks, providing the readers with a holistic understanding of this field. Additionally, it discusses training paradigms of different domains, inference modes, application areas, evaluation metrics, and benchmark datasets, as well as reports the performance of current state-of-the-art methods on these benchmarks, thereby facilitating the readers in selecting the most suitable method for their application. Finally, the survey identifies key challenges, reviews the prevailing trends along with their pros and cons, and identifies promising directions for future research. We cover the literature up to our submission date and will continue to follow the latest works at https://github.com/CNJianLiu/Awesome-Object-Pose-Estimation .
Keyword:
Object pose estimation
Deep learning
Comprehensive survey
3D computer vision
期刊
IF:
9.3
论文数:
3.9K
被引数:
2.8W
机构
引用论文
Match Normalization: Learning-Based Point Cloud Registration for 6D Object Pose Estimation in the Real World匹配归一化：基于学习的方法在真实世界中对点云进行6D物体位姿估计的注册
.png){kind=icon}
PREAI
分享收藏Deep learning for 6D pose estimation of objects-A case study for autonomous driving用于物体6D姿态估计的深度学习-自动驾驶案例研究
OAAI
OAAI分享收藏Deep Learning on Monocular Object Pose Detection and Tracking: A Comprehensive Overview深度学习在单目物体姿态检测与跟踪中的应用综述
OAAI
OAAI分享收藏分享收藏
查看更多内容