Deep Learning based Monocular Depth Prediction: Datasets, Methods and Applications

Abstract

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.

References in corpus (8)

• Depth Map Prediction from a Single Image using a Multi-Scale Deep Network
• DeepVO: Towards End-to-End Visual Odometry with Deep Recurrent Convolutional Neural Networks
• MiniNet: An extremely lightweight convolutional neural network for real-time unsupervised monocular depth estimation
• Recurrent Neural Network for (Un-)supervised Learning of Monocular VideoVisual Odometry and Depth
• Feature-metric Loss for Self-supervised Learning of Depth and Egomotion
• Joint Prediction of Depths, Normals and Surface Curvature from RGB Images using CNNs
• Deformable spatial propagation network for depth completion
• Improving Monocular Depth Estimation by Leveraging Structural Awareness and Complementary Datasets