A Critical Analysis of Image-based Camera Pose Estimation Techniques
It provides a critical overview for researchers in computer vision fields like autonomous driving and AR, but is incremental as it synthesizes existing work without new results.
This survey analyzes image-based camera pose estimation techniques, reviewing methods and comparing their performance on popular datasets to identify areas for algorithmic improvement.
Camera, and associated with its objects within the field of view, localization could benefit many computer vision fields, such as autonomous driving, robot navigation, and augmented reality (AR). In this survey, we first introduce specific application areas and the evaluation metrics for camera localization pose according to different sub-tasks (learning-based 2D-2D task, feature-based 2D-3D task, and 3D-3D task). Then, we review common methods for structure-based camera pose estimation approaches, absolute pose regression and relative pose regression approaches by critically modelling the methods to inspire further improvements in their algorithms such as loss functions, neural network structures. Furthermore, we summarise what are the popular datasets used for camera localization and compare the quantitative and qualitative results of these methods with detailed performance metrics. Finally, we discuss future research possibilities and applications.