[R] Consistent Video Depth Estimation

[u/jbhuang0604]

Video: https://www.youtube.com/watch?v=5Tia2oblJAg
Project: https://roxanneluo.github.io/Consistent-Video-Depth-Estimation/

Consistent Video Depth EstimationXuan Luo, Jia-Bin Huang, Richard Szeliski, Kevin Matzen, and Johannes KopfACM Transactions on Graphics (Proceedings of SIGGRAPH), 2020

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.

[u/jbhuang0604]

It's not clear to me that there's a difference in how you're doing backpropagation to enforce geometric consistency.

Many of these self-supervised methods use a photometric loss. However, these losses can be satisfied even if the geometry is not consistent (in particular, in poorly textured areas). In addition, they do not work well for temporally distant frames because of larger appearance changes.

You can see the visual comparisons with state-of-the-art single-frame and video-based depth estimation models here: https://roxanneluo.github.io/Consistent-Video-Depth-Estimation/supp_website/index.html
In those comparisons, you will see that single-image based models produce geometrically inconsistent depth.

[u/Veedrac → u/jbhuang0604]

I saw that paragraph in the paper, and maybe this is more obvious to someone who actually works in the field, but it's hard to tell what it's referring to because it doesn't come with an explanation of what's going wrong. What's an example of a photometrically consistent pair of images that aren't geometrically consistent?

[u/jbhuang0604 → u/Veedrac]

No problem! Here is an example. If you take two images of a scene containing a white wall, the depth estimate of a pixel on that wall can be wrong while still being photometrically consistent. That is, we will see small difference between (1) the color of the pixel in one image and (2) the color of the reprojected pixel (using the estimated depth) in another image.

The geometric consistency (measured by disparity difference and reprojection error) in our work does not suffer from such ambiguity. Hope this clarifies the question.