Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue

Paper link: https://arxiv.org/pdf/1603.04992v2.pdf

  • single view depth prediction
  • unsupervised framework : without requiring pre-training stage or annotated ground-truth depth
  • analogous to an autoencoder


Approach Overview

  • Make use of pairs of images with a known camera motion, such as stereo pairs.
  • CNN: learn the complex non-linear transformation which converts the image to a depth-map
    • loss: photometric difference between the input (source image and the inverse warped image) differentiable and highly correlated tieh prediction error
  • Interpreted in the context of convolutional autoencoeders

Autoencoder loss

  • notation: i{1N}i \in \{1 \cdots N\}: Training instance
  • notation: {Iii,I2i}\{I_i^i, I_2^i\}: Rectified stereo pair
  • notation: ff: focal length of the two cameras in a single pre-calibrated stereo rig, which capture the image pairs
  • notation: BB: horizontal distance beteen the cameras
  • notation: di(x)d^i(x): the predicted depth of a pixel $x$ in the left of the rig
  • notation: Di(x)=fB/di(x)D^i(x) = fB/d^i(x) the motion of the pixel along the scan-line
  • notation: Iwi=I2i()I_w^i = I^i_2()

Inline math: g(x)dx\int_{-\infty}^\infty g(x) dx

Block math:

g(x)dx \int_{-\infty}^\infty g(x) dx

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