计算机视觉
代表(政治)
特征学习
模式识别(心理学)
深度图
卷积神经网络
作者
Giorgio Giannone,Boris Chidlovskii
出处
期刊:Computer Vision and Pattern Recognition
日期:2019-06-16
卷期号:: 408-415
被引量:2
标识
DOI:10.1109/cvprw.2019.00054
摘要
We propose a new deep learning architecture for the tasks of semantic segmentation and depth prediction from RGB-D images. We revise the state of art based on the RGB and depth feature fusion, where both modalities are assumed to be available at train and test time. We propose a new architecture where the feature fusion is replaced with a common deep representation. Combined with an encoder-decoder type of the network, the architecture can jointly learn models for semantic segmentation and depth estimation based on their common representation. This representation, inspired by multi-view learning, offers several important advantages, such as using one modality available at test time to reconstruct the missing modality. In the RGB-D case, this enables the cross-modality scenarios, such as using depth data for semantically segmentation and the RGB images for depth estimation. We demonstrate the effectiveness of the proposed network on two publicly available RGB-D datasets. The experimental results show that the proposed method works well in both semantic segmentation and depth estimation tasks.
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