Monocular depth estimation based on deep learning: An overview

By Chaoqiang Zhao, Qiyu Sun, Chongzhen Zhang, Yang Tang, Feng Qian. Science China Technological Sciences, 2020

296 citations recorded by the source corpus.

Depth information is important for autonomous systems to perceive environments and estimate their own state. Traditional depth estimation methods, like structure from motion and stereo vision matching, are built on feature correspondences of multiple viewpoints. Meanwhile, the predicted depth maps are sparse. Inferring depth information from a single image (monocular depth estimation) is an ill-posed problem. With the rapid development of deep neural networks, monocular depth estimation based on deep learning has been widely studied recently and achieved promising performance in accuracy. Meanwhile, dense depth maps are estimated from single images by deep neural networks in an end-to-end manner. In order to improve the accuracy of depth estimation, different kinds of network frameworks, loss functions and training strategies are proposed subsequently. Therefore, we survey the current monocular depth estimation methods based on deep learning in this review. Initially, we conclude several widely used datasets and evaluation indicators in deep learning-based depth estimation. Furthermore, we review some representative existing methods according to different training manners: supervised, unsupervised and semi-supervised. Finally, we discuss the challenges and provide some ideas for future researches in monocular depth estimation.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/c1a465b557f22dd0e66810bf5b3729d19eca29ab

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Citations in the corpus

A Brief Overview of ChatGPT: The History, Status Quo and Potential Future Development (2023, background): “Cases are adapted from [1], [3], [11], [17].”
MonoViT: Self-Supervised Monocular Depth Estimation with a Vision Transformer (2022, background): “However, considering the high cost of collecting dense depth labels for this purpose, self-supervised monocular depth estimation [16, 71] has emerged in the literature enabling signiﬁ-cant progress in recent years [66].”
BinsFormer: Revisiting Adaptive Bins for Monocular Depth Estimation (2022, background): “M ONOCULAR depth estimation (MDE) is a fundamental yet challenging task in computer vision, which requires the algorithm to predict each pixel’s depth within a single input RGB image [1].”
A Comprehensive Survey of Visual SLAM Algorithms (2022, methodology): “DynaSLAM II [127] is another relevant method that incorporates semantic segmentation to track dynamic objects.”