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Unsupervised framework for depth estimation and camera motion prediction from video

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Academic year: 2021

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Figure

Fig. 1. Overview of our method. Training samples consist of unlabeled stereo image sequences captured from a binocular camera which does not provide the pose of image  sequences
Fig. 2. Architecture of depth CNN. It is an encoder-decoder model, the width and height of each cube indicate the spatial dimensions of the output feature map respectively
Fig. 3. Network architecture of the pose CNN. It is an encoder model, the output of this network is four matrices corresponding to the transformations from the source  images to the target images
Fig. 4. For depth CNN, we use stereo image pairs as input data to generate corresponding disparity maps
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