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DeepV2D: Video to Depth with Differentiable Structure from Motion

2019-04-19
Zachary Teed, Jia Deng

Abstract

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: camera motion estimation and depth estimation. During inference, motion and depth estimation are alternated and quickly converge to accurate depth. Code is available https://github.com/princeton-vl/DeepV2D.

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URL

http://arxiv.org/abs/1812.04605

PDF

http://arxiv.org/pdf/1812.04605


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