  Over the last two decades, deep learning has transformed the field of
computer vision. Deep convolutional networks were successfully applied to learn
different vision tasks such as image classification, image segmentation, object
detection and many more. By transferring the knowledge learned by deep models
on large generic datasets, researchers were further able to create fine-tuned
models for other more specific tasks. Recently this idea was applied for
regressing the absolute camera pose from an RGB image. Although the resulting
accuracy was sub-optimal, compared to classic feature-based solutions, this
effort led to a surge of learning-based pose estimation methods. Here, we
review deep learning approaches for camera pose estimation. We describe key
methods in the field and identify trends aiming at improving the original deep
pose regression solution. We further provide an extensive cross-comparison of
existing learning-based pose estimators, together with practical notes on their
execution for reproducibility purposes. Finally, we discuss emerging solutions
and potential future research directions.
