Introduction to Camera Pose Estimation with Deep Learning

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.

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Cited by in corpus (7)

• LENS: Localization enhanced by NeRF synthesis
• Direct-PoseNet: Absolute Pose Regression with Photometric Consistency
• DeepI2P: Image-to-Point Cloud Registration via Deep Classification
• Initialisation of Autonomous Aircraft Visual Inspection Systems via CNN-Based Camera Pose Estimation
• Wide-Baseline Multi-Camera Calibration using Person Re-Identification
• CNN-Based Camera Pose Estimation and Localisation of Scan Images for Aircraft Visual Inspection
• Estimating 3D Camera Pose from 2D Pedestrian Trajectories