Introduction to Camera Pose Estimation with Deep Learning

TL;DR

This paper reviews deep learning methods for camera pose estimation from RGB images, highlighting key approaches, trends, and future research directions in the field.

Contribution

It provides a comprehensive review and comparison of existing deep learning-based camera pose estimation techniques, including practical insights for reproducibility.

Findings

Deep learning methods have advanced camera pose estimation but still lag behind classic solutions.

The paper identifies key trends and emerging solutions in the field.

It offers practical notes for implementing and reproducing existing methods.

Abstract

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.