DeepV2D: Video to Depth with Differentiable Structure from Motion

ICLR 2020 Zachary TeedJia Deng

We propose DeepV2D, an end-to-end deep learning architecture for predicting depth from video. DeepV2D combines the representation ability of neural networks with the geometric principles governing image formation... (read more)

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