期刊
SENSORS
卷 19, 期 8, 页码 -出版社
MDPI
DOI: 10.3390/s19081795
关键词
depth estimation; semantic segmentation; convolutional neural networks; hybrid architecture
资金
- Spanish Ministerio de Economia y Competitividad [MALEGRA TEC2016-75976-R]
- European Regional Development Fund (ERDF)
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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