Consistent Video Depth Estimation

Xuan Luo

University of Washington

Jia-Bin Huang

Virginia Tech

Richard Szeliski

Facebook

Kevin Matzen

Facebook

Johannes Kopf

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Overview Video


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Abstract

We present an algorithm for reconstructing dense, geometrically consistent depth for all pixels in a monocular video. We leverage a conventional structure-from-motion reconstruction to establish geometric constraints on pixels in the video. Unlike the ad-hoc priors in classical reconstruction, we use a learning-based prior, i.e., a convolutional neural network trained for single-image depth estimation. At test time, we fine-tune this network to satisfy the geometric constraints of a particular input video, while retaining its ability to synthesize plausible depth details in parts of the video that are less constrained. We show through quantitative validation that our method achieves higher accuracy and a higher degree of geometric consistency than previous monocular reconstruction methods. Visually, our results appear more stable. Our algorithm is able to handle challenging hand-held captured input videos with a moderate degree of dynamic motion. The improved quality of the reconstruction enables several applications, such as scene reconstruction and advanced video-based visual effects.

Results

BibTeX

@article{Luo-VideoDepth-2020,
  author    = {Luo, Xuan and Huang, Jia{-}Bin and Szeliski, Richard and Matzen, Kevin and Kopf, Johannes},
  title     = {Consistent Video Depth Estimation},
  booktitle = {ACM Transactions on Graphics (Proceedings of ACM SIGGRAPH)},
  publisher = {ACM},
  volume = {39},
  number = {4},
  year = {2020}
}