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Improving Depth Estimation by Embedding Semantic Segmentation: A Hybrid CNN Model
| Content Provider | MDPI |
|---|---|
| Author | Jos, é E. Valdez-Rodríguez Edgardo, Felipe-Riverón Marco, A. Moreno-Armendáriz Calvo, Hiram |
| Copyright Year | 2022 |
| Description | Single image depth estimation works fail to separate foreground elements because they can easily be confounded with the background. To alleviate this problem, we propose the use of a semantic segmentation procedure that adds information to a depth estimator, in this case, a 3D Convolutional Neural Network (CNN)—segmentation is coded as one-hot planes representing categories of objects. We explore 2D and 3D models. Particularly, we propose a hybrid 2D–3D CNN architecture capable of obtaining semantic segmentation and depth estimation at the same time. We tested our procedure on the SYNTHIA-AL dataset and obtained |
| Starting Page | 1669 |
| e-ISSN | 14248220 |
| DOI | 10.3390/s22041669 |
| Journal | Sensors |
| Issue Number | 4 |
| Volume Number | 22 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-02-21 |
| Access Restriction | Open |
| Subject Keyword | Sensors Artificial Intelligence Depth Estimation Hybrid Convolutional Neural Networks Semantic Segmentation 3d Cnn |
| Content Type | Text |
| Resource Type | Article |
