What is the difference between Optical flow estimation and Disparity estimation?

Question

I am trying to understand some computer vision topics. One main difference I observed between these two is that In optical flow, the 2nd image is often at time (t+1) whereas in disparity estimation, its often the same time-step unless one is having a static view and using single non-stereo camera. Is there any other difference and their respective implications?

Answer 1

As you pointed out, Optical flow represents the displacement of pixels between an image at time t and at time t+1 whereas disparity estimation is the displacement of a pixel between one camera and another. Strictly speaking these two tasks could be considered identical.

However, in practice, disparity is computed using a "right" and a "left" camera which are horizontally aligned. Therefore, the disparity is only horizontal (and in a single direction due to the laws of optics) and can be represented by a heatmap.

On the contrary, Optical flow is a 2D vector field in which vectors can take any value.

In machine learning, this distinction mainly changes the dimension of the output (1D for disparity and 2D for Optical flow) to predict as well as its scale (positive for disparity vs all real numbers for Optical flow).

I hope my answer was clear:)