Deep Learning based Monocular Depth Prediction: Datasets, Methods and Applications
It synthesizes existing research for practitioners and researchers in computer vision, but is incremental as it is a review paper.
This paper provides a comprehensive survey of monocular depth estimation using deep learning, covering datasets, methods, and applications, but does not present new experimental results or concrete performance numbers.
Estimating depth from RGB images can facilitate many computer vision tasks, such as indoor localization, height estimation, and simultaneous localization and mapping (SLAM). Recently, monocular depth estimation has obtained great progress owing to the rapid development of deep learning techniques. They surpass traditional machine learning-based methods by a large margin in terms of accuracy and speed. Despite the rapid progress in this topic, there are lacking of a comprehensive review, which is needed to summarize the current progress and provide the future directions. In this survey, we first introduce the datasets for depth estimation, and then give a comprehensive introduction of the methods from three perspectives: supervised learning-based methods, unsupervised learning-based methods, and sparse samples guidance-based methods. In addition, downstream applications that benefit from the progress have also been illustrated. Finally, we point out the future directions and conclude the paper.