Research Article
A Review of Monocular Depth Estimation Methods Based on Deep Learning
@INPROCEEDINGS{10.4108/eai.24-3-2022.2318957, author={Huma Farooq and Manzoor Ahmad Chachoo}, title={A Review of Monocular Depth Estimation Methods Based on Deep Learning }, proceedings={Proceedings of the 3rd International Conference on ICT for Digital, Smart, and Sustainable Development, ICIDSSD 2022, 24-25 March 2022, New Delhi, India}, publisher={EAI}, proceedings_a={ICIDSSD}, year={2023}, month={5}, keywords={cnn depth estimation monocular kitti dataset nyu dataset}, doi={10.4108/eai.24-3-2022.2318957} }- Huma Farooq
Manzoor Ahmad Chachoo
Year: 2023
A Review of Monocular Depth Estimation Methods Based on Deep Learning
ICIDSSD
EAI
DOI: 10.4108/eai.24-3-2022.2318957
Abstract
In applications like autonomous vehicle driving or robot maneuverability, Precise depth estimation from images is vital for understanding the scene and its reconstruction. Traditional depth estimation techniques are based on component correspondences of several viewpoints. Using a single image to extract depth information is challenging. Monocular estimation of depth based on deep neural networks has been effectively employed by researchers and has improved accuracy. As a result, several existing network frameworks and training approaches are examined in order to improve depth estimation precision. The current approaches of monocular estimation of depth based on deep neural network learning are reviewed in this paper. The paper examines a variety of learning strategies, as well as datasets for Monocular depth estimates and challenges.