  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, \emph{i.e.}, instance-level, category-level, and unseen 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 also keep tracing the latest works
at https://github.com/CNJianLiu/Awesome-Object-Pose-Estimation.
