Slide 19: I don't understand the line at the bottom of the slide "Train a regressor to predict depth --> Noisy predictions" Does this mean that if we use ML, the results will be noisy?
Slides 20 and 21: I don't understand the use of the Markov Random Field. Based on the slide, I think it means connecting a graph to the graph of the image, but I don't understand the meaning of the equation and how it enforces consistency.
And it leads to the next slides, what is the difference between Deep Learning with MRF and without?