EPI-Patch Based Convolutional Neural Network for Depth Estimation on 4D Light Field

Author(s):  
Yaoxiang Luo ◽  
Wenhui Zhou ◽  
Junpeng Fang ◽  
Linkai Liang ◽  
Hua Zhang ◽  
...  
Author(s):  
Agota Faluvegi ◽  
Quentin Bolsee ◽  
Sergiu Nedevschi ◽  
Vasile-Teodor Dadarlat ◽  
Adrian Munteanu

2020 ◽  
Vol 6 ◽  
pp. 682-696 ◽  
Author(s):  
Jiayong Peng ◽  
Zhiwei Xiong ◽  
Yicheng Wang ◽  
Yueyi Zhang ◽  
Dong Liu

2018 ◽  
Vol 78 (20) ◽  
pp. 29211-29227 ◽  
Author(s):  
Zhicheng Lu ◽  
Henry W. F. Yeung ◽  
Qiang Qu ◽  
Yuk Ying Chung ◽  
Xiaoming Chen ◽  
...  

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1795 ◽  
Author(s):  
Xiao Lin ◽  
Dalila Sánchez-Escobedo ◽  
Josep R. Casas ◽  
Montse Pardàs

Semantic segmentation and depth estimation are two important tasks in computer vision, and many methods have been developed to tackle them. Commonly these two tasks are addressed independently, but recently the idea of merging these two problems into a sole framework has been studied under the assumption that integrating two highly correlated tasks may benefit each other to improve the estimation accuracy. In this paper, depth estimation and semantic segmentation are jointly addressed using a single RGB input image under a unified convolutional neural network. We analyze two different architectures to evaluate which features are more relevant when shared by the two tasks and which features should be kept separated to achieve a mutual improvement. Likewise, our approaches are evaluated under two different scenarios designed to review our results versus single-task and multi-task methods. Qualitative and quantitative experiments demonstrate that the performance of our methodology outperforms the state of the art on single-task approaches, while obtaining competitive results compared with other multi-task methods.


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