ReviewSensors (Basel, Switzerland)2022
Monocular Depth Estimation Using Deep Learning: A Review.
Review in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
18 citing papers in PubMed.
- VISIOCPR: Monocular Vision-Based CPR Training System with Human-Computer Collaborative Feedback.Sensors (Basel, Switzerland) · 2026Article
- EFDepth: A Monocular Depth Estimation Model for Multi-Scale Feature Optimization.Sensors (Basel, Switzerland) · 2025Article
- LiDAR Point Cloud Colourisation Using Multi-Camera Fusion and Low-Light Image Enhancement.Sensors (Basel, Switzerland) · 2025Article
- Human-like monocular depth biases in deep neural networks.PLoS computational biology · 2025Article
- SfMDiffusion: self-supervised monocular depth estimation in endoscopy based on diffusion models.International journal of computer assisted radiology and surgery · 2025Article
- MonoDFNet: Monocular 3D Object Detection with Depth Fusion and Adaptive Optimization.Sensors (Basel, Switzerland) · 2025Article
- Effect of Depth Band Replacement on Red, Green and Blue Image for Deep Learning Weed Detection.Sensors (Basel, Switzerland) · 2024Article
- Residual Vision Transformer and Adaptive Fusion Autoencoders for Monocular Depth Estimation.Sensors (Basel, Switzerland) · 2024Article
- Fusing Events and Frames with Coordinate Attention Gated Recurrent Unit for Monocular Depth Estimation.Sensors (Basel, Switzerland) · 2024Article
- Impact of Perception Errors in Vision-Based Detection and Tracking Pipelines on Pedestrian Trajectory Prediction in Autonomous Driving Systems.Sensors (Basel, Switzerland) · 2024Article
- Monocular Absolute Depth Estimation from Motion for Small Unmanned Aerial Vehicles by Geometry-Based Scale Recovery.Sensors (Basel, Switzerland) · 2024Article
- Synthetic Data Enhancement and Network Compression Technology of Monocular Depth Estimation for Real-Time Autonomous Driving System.Sensors (Basel, Switzerland) · 2024Article
- LNMVSNet: A Low-Noise Multi-View Stereo Depth Inference Method for 3D Reconstruction.Sensors (Basel, Switzerland) · 2024Article
- A Survey of 6DoF Object Pose Estimation Methods for Different Application Scenarios.Sensors (Basel, Switzerland) · 2024Review
- Application of Machine Vision Techniques in Low-Cost Devices to Improve Efficiency in Precision Farming.Sensors (Basel, Switzerland) · 2024Article
- Adaptable 2D to 3D Stereo Vision Image Conversion Based on a Deep Convolutional Neural Network and Fast Inpaint Algorithm.Entropy (Basel, Switzerland) · 2023Article
- Nested DWT-Based CNN Architecture for Monocular Depth Estimation.Sensors (Basel, Switzerland) · 2023Article
- Parallax Inference for Robust Temporal Monocular Depth Estimation in Unstructured Environments.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In current decades, significant advancements in robotics engineering and autonomous vehicles have improved the requirement for precise depth measurements. Depth estimation (DE) is a traditional task in computer vision that can be appropriately predicted by applying numerous procedures. This task is vital in disparate applications such as augmented reality and target tracking. Conventional monocular DE (MDE) procedures are based on depth cues for depth prediction. Various deep learning techniques have demonstrated their potential applications in managing and supporting the traditional ill-posed problem. The principal purpose of this paper is to represent a state-of-the-art review of the current developments in MDE based on deep learning techniques. For this goal, this paper tries to highlight the critical points of the state-of-the-art works on MDE from disparate aspects. These aspects include input data shapes and training manners such as supervised, semi-supervised, and unsupervised learning approaches in combination with applying different datasets and evaluation indicators. At last, limitations regarding the accuracy of the DL-based MDE models, computational time requirements, real-time inference, transferability, input images shape and domain adaptation, and generalization are discussed to open new directions for future research.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.