scholarly journals An Adaptive Unsupervised Learning Framework for Monocular Depth Estimation

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 148142-148151
Author(s):  
Delong Yang ◽  
Xunyu Zhong ◽  
Lixiong Lin ◽  
Xiafu Peng
Keyword(s):  
Unsupervised Learning ◽  
Depth Estimation ◽  
Learning Framework ◽  
Monocular Depth

Monocular depth estimation based on unsupervised learning

2019 ◽  
Author(s):  
Wan Liu ◽  
Yan Sun ◽  
XuCheng Wang ◽  
Lin Yang ◽  
Zhenrong Zheng
Keyword(s):  
Unsupervised Learning ◽  
Depth Estimation ◽  
Monocular Depth

scholarly journals Absolute Distance Prediction Based on Deep Learning Object Detection and Monocular Depth Estimation Models

2021 ◽  
Author(s):  
Armin Masoumian ◽  
David G.F. Marei ◽  
Saddam Abdulwahab ◽  
Julián Cristiano ◽  
Domenec Puig ◽  
...  
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Distance Estimation ◽  
Depth Estimation ◽  
Depth Image ◽  
Absolute Distance ◽  
Learning Framework ◽  
Depth Images ◽  
Monocular Depth ◽  
Distance Prediction

Determining the distance between the objects in a scene and the camera sensor from 2D images is feasible by estimating depth images using stereo cameras or 3D cameras. The outcome of depth estimation is relative distances that can be used to calculate absolute distances to be applicable in reality. However, distance estimation is very challenging using 2D monocular cameras. This paper presents a deep learning framework that consists of two deep networks for depth estimation and object detection using a single image. Firstly, objects in the scene are detected and localized using the You Only Look Once (YOLOv5) network. In parallel, the estimated depth image is computed using a deep autoencoder network to detect the relative distances. The proposed object detection based YOLO was trained using a supervised learning technique, in turn, the network of depth estimation was self-supervised training. The presented distance estimation framework was evaluated on real images of outdoor scenes. The achieved results show that the proposed framework is promising and it yields an accuracy of 96% with RMSE of 0.203 of the correct absolute distance.

Unsupervised Monocular Depth Estimation for Autonomous Driving

2019 ◽  
pp. 128
Author(s):  
Chih-Shuan Huang ◽  
Wan-Nung Tsung ◽  
Wei-Jong Yang ◽  
Chin-Hsing Chen
Keyword(s):  
Depth Estimation ◽  
Autonomous Driving ◽  
Monocular Depth

scholarly journals Unsupervised Learning-Based Depth Estimation-Aided Visual SLAM Approach

2019 ◽  
Vol 39 (2) ◽  
pp. 543-570 ◽  
Author(s):  
Mingyang Geng ◽  
Suning Shang ◽  
Bo Ding ◽  
Huaimin Wang ◽  
Pengfei Zhang
Keyword(s):  
Unsupervised Learning ◽  
Depth Estimation ◽  
Visual Slam

Hierarchical Object Relationship Constrained Monocular Depth Estimation.

2021 ◽  
pp. 108116
Author(s):  
Shuai Li ◽  
Jiaying Shi ◽  
Wenfeng Song ◽  
Aimin Hao ◽  
Hong Qin
Keyword(s):  
Depth Estimation ◽  
Monocular Depth ◽  
Object Relationship
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