DeepV2D: Video to Depth with Differentiable Structure from Motion [article]

Zachary Teed, Jia Deng
2020 arXiv   pre-print
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. We compose a collection of classical geometric algorithms, which are converted into trainable modules and combined into an end-to-end differentiable architecture. DeepV2D interleaves two stages: motion estimation and depth estimation. During inference, motion and depth estimation are
more » ... lternated and converge to accurate depth. Code is available
arXiv:1812.04605v4 fatcat:sgmbcd3tvjbcxitja6kc5jw2pm