Paper Info
Title
Learning Depth Estimation From Memory Infusing Monocular Cues: A Generalization Prediction Approach
Abstract
Depth estimation from a single image is a challenging task, yet this field has a promising prospect in automatic driving and augmented reality. However, the prediction accuracy is degraded significantly when the trained network is transferred from the training dataset to real scenarios. To solve this issue, we propose MonoMeMa, a novel deep architecture based on the human monocular cue, which means humans can perceive depth information with one eye through the relative size of objects, light and shadow, etc. based on previous visual experience. Our method simulates the process of the formation and utilization of human monocular visual memory, including three steps: Firstly, MonoMeMa perceives and extracts real-world objects feature vectors (encoding). Then, it maintains and replaces the extracted feature vector over time (storing). Finally, MonoMeMa combines query objects feature vectors and memory to inference depth information (retrieving). According to the simulation results, our model shows the state-of-the-art results on the KITTI driving dataset. Moreover, MonoMema exhibits remarkable generalization performance when our model is migrated to other driving datasets without any finetune.
Year
DOI
Venue
2022
10.1109/ACCESS.2022.3151108
IEEE ACCESS
Keywords
DocType
Volume
Estimation, Feature extraction, Task analysis, Cameras, Training, Deep learning, Visualization, Long short-term memory (LSTM), monocular depth estimiation, multi-layer perceptron (MLP), region proposal network (RPN)
Journal
10
ISSN
Citations 
PageRank 
2169-3536
0
0.34
References 
Authors
0
7
Name
Order
Citations
PageRank
Yakun Zhou101.01
Jinting Luo211.06
Musen Hu300.34
Tingyong Wu400.34
Jinkuan Zhu500.34
Xingzhong Xiong602.03
Jienan Chen78413.64