> I have a feeling people are going to reply without reading this through and assume the author poses a Deep Learning vs Classic CV sort of argument, in a deep-

> I have a feeling people are going to reply without reading this through and assume the author poses a Deep Learning vs Classic CV sort of argument, in a deep-learning-is-overrated sort of way. Whereas it seems to me he is merely saying Deep Learning should be informed by Classic CV. Thank you for saying this! You were indeed prescient - nearly all the comments so far at total non-sequiturs based on what the article actually shows. Folks, please take a look at the actual GC-Net architecture, which is very nicely shown in the last figure in the post. As you'll see, it's still an end-to-end learnt deep learning model. The extremely neat trick is to come up with a way to represent the cost volume piece (that connects the 2d and 3d parts of the CNN) as a differentiable function, which allows it to be part of an end-to-end learnt neural network. This approach, of finding differentiable forms (often approximations) of domain-specific loss functions, transformations, etc so they can be inserted into neural nets, is a very powerful and increasingly widely used technique. It's not about replacing domain knowledge with deep learning, and it's definitely not about replacing deep learning with domain knowledge - it's all about using both. So in this case, the research is showing how to use geometry plus deep learning, not instead of deep learning.